├── .gitignore
├── README.md
├── build.sh
├── build
    └── templater.js
├── data
    ├── assignments.yml
    ├── lectures.yml
    ├── site-instructions.md
    └── this-week.yml
├── deploy.sh
├── favicon.ico
├── fonts
    ├── glyphicons-halflings-regular.woff
    └── glyphicons-halflings-regular.woff2
├── images
    ├── barber-1x.jpg
    ├── barber-2x.jpg
    ├── barber-3x.jpg
    ├── barber-original.jpg
    ├── bishop-1x.jpg
    ├── bishop-2x.jpg
    ├── bishop-3x.jpg
    ├── bishop-original.jpg
    ├── boyd-1x.jpg
    ├── boyd-2x.jpg
    ├── boyd-original.jpg
    ├── geron-original.jpg
    ├── hastie-1x.png
    ├── hastie-2x.jpg
    ├── hastie-3x.jpg
    ├── hastie-original.jpg
    ├── james-1x.jpg
    ├── james-2x.jpg
    ├── james-3x.jpg
    ├── james-original.jpg
    ├── murphy-1x.jpg
    ├── murphy-2x.jpg
    ├── murphy-3x.jpg
    ├── murphy-original.jpg
    ├── people
    │   ├── aakash.JPG
    │   ├── david.jpg
    │   ├── julia.jpg
    │   ├── mihir.jpg
    │   ├── mingsi.jpg
    │   ├── sanyam.jpg
    │   ├── sreyas.png
    │   ├── tingyan.jpeg
    │   ├── xintian.jpeg
    │   └── yi.jpg
    ├── provost-fawcett-original.jpg
    └── shalev-shwartz-original.jpg
├── index.hbs
├── package-lock.json
├── package.json
├── scripts
    └── navigation.js
├── styles
    ├── colors.styl
    ├── phone.styl
    ├── style.styl
    └── tablet-and-phone.styl
├── syllabusDS-GA1003-Spring2019.docx
├── syllabusDS-GA1003-Spring2019.pdf
└── templates
    ├── _assignment-details.hbs
    ├── assignments.hbs
    ├── lectures.hbs
    └── this-week.hbs


/.gitignore:
--------------------------------------------------------------------------------
 1 | # Node
 2 | package-lock.json
 3 | node_modules
 4 | 
 5 | # Misc
 6 | styles
 7 | index.html
 8 | 
 9 | # Build site
10 | out
11 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # DS-GA 1003 Website
 2 | 
 3 | ## Editing, rebuilding, and deploying this page
 4 | 
 5 | ### Building locally: quickstart
 6 | 
 7 | Be sure to have [Node.js](https://iojs.org/) 7.x+ installed.
 8 | 
 9 | Run `npm install` in the project root. This will install some build tools we use.
10 | 
11 | Run `npm run build-in-place` to do the templating and stylesheet compilation in-place. You can then view the site by simply opening `index.html`.
12 | 
13 | Run `./build.sh` to do a local build into the `out/` directory. You can then preview the site at `out/index.html`. This should usually be the same as just `index.html`, but it is good to check before committing, since this "local deploy" process is slightly more complicated than the in-place build process.
14 | 
15 | ### How to edit content
16 | The file index.hbs is usually what you should edit, basically as though you were editing an HTML file. The final HTML is generated with some JavaScript processing that pulls in data from the YAML files in the data directory -- basically the information in the lectures and assignments tables. 
17 | 
18 | ### Deployment
19 | Run the script `./deploy.sh` from the root directory of the project to build and deploy the page to GitHub.  The script does the following: 1) Pulls down the gh-pages branch into a folder called "out" in the project root directory. (GitHub serves webpages from gh-pages branches.) Then it runs `npm run build` which compiles the page and puts the output into out. Then the revised out folder is committed and pushed back to the gh-pages branch, ready to be served.
20 | 
21 | ### Technologies used
22 | 
23 | [Stylus](https://learnboost.github.io/stylus/) is used for styling.
24 | 
25 | [Handlebars](http://handlebarsjs.com/) is used for templating. `index.hbs` is minimally templated, mostly delegating to the partials in `templates/`. Those pull their data from `data/`. The logic that ties them all together is in `build/templater.js`.
26 | 
27 | The site is intended to be responsive, which we accomplish with per-device stylesheets and media queries in the HTML.
28 | 
29 | ### Things to Keep in Mind
30 | 
31 | While editing you should be using an [EditorConfig](http://editorconfig.org/) plugin for your text editor to enforce a few basic stylistic things.
32 | 
33 | We are trying to maintain a reasonable HTML document outline (so, don't use `<section>` as if it were `<div>`). To preview the document outline, use the [HTML 5 Outliner tool](https://gsnedders.html5.org/outliner/).
34 | 


--------------------------------------------------------------------------------
/build.sh:
--------------------------------------------------------------------------------
 1 | #!/bin/bash
 2 | set -e # exit with nonzero exit code if anything fails
 3 | 
 4 | ## TODO migrate to gulp probably
 5 | 
 6 | # clear and re-create the out directory
 7 | rm -f index.html
 8 | rm -rf styles/*.css
 9 | 
10 | npm run build-in-place
11 | 
12 | mkdir -p out/styles/
13 | cp index.html favicon.ico out/
14 | cp styles/*.css out/styles/
15 | 
16 | cp -r images fonts scripts out/
17 | 
18 | echo ""
19 | find out/ -print
20 | echo ""
21 | 


--------------------------------------------------------------------------------
/build/templater.js:
--------------------------------------------------------------------------------
  1 | 'use strict';
  2 | const path = require('path');
  3 | const yaml = require('js-yaml');
  4 | const fs = require('fs');
  5 | const handlebarsFactory = require('handlebars');
  6 | const moment = require('moment');
  7 | const assert = require('assert');
  8 | const toSlug = require('slugg');
  9 | 
 10 | const SLIDES = 'Slides';
 11 | const NOTES = 'Notes';
 12 | const REFERENCES = 'References';
 13 | 
 14 | const TEMPLATES_DIR = path.resolve(__dirname, '../templates');
 15 | 
 16 | doTemplating(process.argv[2], process.argv[3]);
 17 | 
 18 | function doTemplating(input, output) {
 19 |     const handlebars = handlebarsFactory.create();
 20 |     registerHelpers(handlebars);
 21 |     registerPartials(handlebars);
 22 | 
 23 |     const template = compileTemplate(handlebars, input);
 24 |     const documents = parseDocuments();
 25 | 
 26 |     // Uncomment to see documents; useful while tweaking the templates
 27 |     // console.log(require("util").inspect(documents, { depth: Infinity }));
 28 | 
 29 |     fs.writeFileSync(output, template(documents));
 30 | }
 31 | 
 32 | function compileTemplate(handlebars, input) {
 33 |     return handlebars.compile(fs.readFileSync(input, { encoding: 'utf-8' }));
 34 | }
 35 | 
 36 | function parseDocuments() {
 37 |     const lectures = yaml.safeLoad(fs.readFileSync(path.resolve(__dirname, '../data/lectures.yml')));
 38 | 
 39 |     // Normalize the data
 40 |     for (const lecture of lectures) {
 41 |         for (const event of Object.values(lecture.Events)) {
 42 |             ensureArrayExists(event, SLIDES);
 43 |             ensureArrayExists(event, NOTES);
 44 |             ensureArrayExists(event, REFERENCES);
 45 |         }
 46 |     }
 47 | 
 48 |     let assignmentsFrontmatter, assignments;
 49 |     let i = 0;
 50 |     yaml.safeLoadAll(fs.readFileSync(path.resolve(__dirname, '../data/assignments.yml')), doc => {
 51 |         switch (i) {
 52 |             case 0:
 53 |                 assignmentsFrontmatter = doc;
 54 |                 break;
 55 |             case 1:
 56 |                 assignments = doc;
 57 |                 break;
 58 |             default:
 59 |                 throw new Error('Cannot have more than two documents in assignments.yaml');
 60 |         }
 61 |         ++i;
 62 |     });
 63 | 
 64 |     for (const assignment of assignments) {
 65 |         if (!assignment.PDF && !assignment.ZIP) {
 66 |             assignment.noFiles = true;
 67 |         }
 68 |     }
 69 | 
 70 |     let thisWeek = yaml.safeLoad(fs.readFileSync(path.resolve(__dirname, '../data/this-week.yml')));
 71 | 
 72 |     if (thisWeek === null) {
 73 |         thisWeek = { lecture: null };
 74 |     } else {
 75 |         // Pull data from lectures and assignments into thisWeek:
 76 |         const thisWeekLecture = lectures.find(l => l.Title === thisWeek['Lecture/Lab']);
 77 |         const thisWeekAssignment = assignments.find(a => a.Label === thisWeek.Assignment);
 78 | 
 79 |         if (thisWeekLecture === undefined) {
 80 |             throw new Error(`Could not find entry in lectures.yml with Title "${thisWeek['Lecture/Lab']}" specified in ` +
 81 |                 `this-week.yaml`);
 82 |         }
 83 |         if (thisWeekAssignment === undefined) {
 84 |             throw new Error(`Could not find entry in assignments.yml with Label "${thisWeek['Assignment']}" specified in ` +
 85 |                 `this-week.yaml`);
 86 |         }
 87 | 
 88 |         thisWeek.lecture = thisWeekLecture;
 89 |         thisWeek.assignment = thisWeekAssignment;
 90 |     }
 91 | 
 92 |     return { lectures, thisWeek, assignmentsFrontmatter, assignments };
 93 | }
 94 | 
 95 | function registerPartials(handlebars) {
 96 |     for (const filename of fs.readdirSync(TEMPLATES_DIR)) {
 97 |         const filePath = path.resolve(TEMPLATES_DIR, filename);
 98 |         const partialName = path.basename(filename, '.hbs');
 99 |         const contents = fs.readFileSync(filePath, { encoding: 'utf-8' });
100 | 
101 |         handlebars.registerPartial(partialName, contents);
102 |     }
103 | }
104 | 
105 | function registerHelpers(handlebars) {
106 |     handlebars.registerHelper('date', d => moment.utc(new Date(d).toISOString()).format('MMMM Do'));
107 |     handlebars.registerHelper('shortDate', d => moment.utc(new Date(d).toISOString()).format('MMM D'));
108 |     handlebars.registerHelper('maybeLink', v => {
109 |         if (typeof v === 'string') {
110 |             return v;
111 |         }
112 | 
113 |         assert (typeof v === 'object' && v !== null, 'Links must be either strings or objects');
114 | 
115 |         const keys = Object.keys(v);
116 |         assert(keys.length === 1, 'Link objects must have a single key');
117 |         const key = keys[0];
118 | 
119 |         return new handlebars.SafeString('<a href="' + v[key] + '">' + key + '</a>');
120 |     });
121 |     handlebars.registerHelper('lectureSlug', l => 'lecture-' + toSlug(l.Title));
122 |     handlebars.registerHelper('assignmentSlug', l => 'assignment-' + toSlug(l.Label));
123 | }
124 | 
125 | function ensureArrayExists(obj, prop) {
126 |     if (!(prop in obj)) {
127 |         obj[prop] = [];
128 |     }
129 | }
130 | 
131 | function copyArrayInto(source, dest, keyName) {
132 |     if (source && source[keyName]) {
133 |         dest[keyName].push(...source[keyName]);
134 |     }
135 | }
136 | 


--------------------------------------------------------------------------------
/data/assignments.yml:
--------------------------------------------------------------------------------
 1 | Due time: 11:59 PM
 2 | Submit link: "https://gradescope.com"
 3 | ---
 4 | -
 5 |   Label: Homework 0
 6 |   Description: Typesetting your homework
 7 |   Due: 2000-01-01
 8 |   PDF: {hw0.pdf: "https://davidrosenberg.github.io/mlcourse/Archive/2017/Homework/hw0.pdf"}
 9 |   ZIP: {hw0.zip: "https://davidrosenberg.github.io/mlcourse/Archive/2017/Homework/hw0.zip"}
10 | -
11 |   Label: Homework 1
12 |   Description: 
13 |   Due: 2019-02-09
14 |   PDF: {hw1.pdf: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw1.pdf"}
15 |   ZIP: {hw1.zip: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw1.zip"}
16 | -
17 |   Label: Homework 2
18 |   Description: 
19 |   Due: 2019-02-18
20 |   PDF: {hw2.pdf: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw2.pdf"}
21 |   ZIP: {hw2.zip: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw2.zip"}
22 | -
23 |   Label: Homework 3
24 |   Description: 
25 |   Due: 2019-02-25
26 |   PDF: {hw3.pdf: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw3.pdf"}
27 |   ZIP: {hw3.zip: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw3.zip"}
28 | -
29 |   Label: Homework 4
30 |   Description: 
31 |   Due: 2019-03-08
32 |   PDF: {hw4.pdf: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw4.pdf"}
33 |   ZIP: {hw4.zip: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw4.zip"}
34 | -
35 |   Label: Homework 5
36 |   Description: 
37 |   Due: 2019-04-05
38 |   PDF: {hw5.pdf: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw5.pdf"}
39 |   ZIP: {hw5.zip: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw5.zip"}
40 | -
41 |   Label: Homework 6
42 |   Description: 
43 |   Due: 2019-04-29
44 |   PDF: {hw6.pdf: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw6.pdf"}
45 |   ZIP: {hw6.zip: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw6.zip"}
46 | -
47 |   Label: Homework 7
48 |   Description: 
49 |   Due: 2019-05-10
50 |   PDF: {hw7.pdf: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw7.pdf"}
51 |   ZIP: {hw7.zip: " https://davidrosenberg.github.io/mlcourse/Archive/2019/Homework/hw7.zip"}
52 | 


--------------------------------------------------------------------------------
/data/lectures.yml:
--------------------------------------------------------------------------------
  1 | -
  2 |   Title: Week 0
  3 |   Events:
  4 |     2000-01-01:
  5 |       Label: ML Prereqs
  6 | #      Video: "https://youtu.be/U6M0m9c9_Js"
  7 |       Slides:
  8 |         - {Black Box Machine Learning: "https://bloomberg.github.io/foml/#lecture-1-black-box-machine-learning"}
  9 |         - {Evaluating Classifiers: "https://bloomberg.github.io/foml/#lecture-14-performance-evaluation"}
 10 |       Notes:
 11 |         - {Géron's Machine Learning Landscape: "https://github.com/ageron/handson-ml/blob/master/01_the_machine_learning_landscape.ipynb"}
 12 |         - {Géron's End-to-End Machine Learning: "https://github.com/ageron/handson-ml/blob/master/02_end_to_end_machine_learning_project.ipynb"}
 13 |       References:
 14 |         - Provost and Fawcett book
 15 |         - {"Géron Ch 1,2": "https://getit.library.nyu.edu/go/9443019"}
 16 | -
 17 |   Title: Week 1
 18 |   Events:
 19 |     2019-01-29:
 20 |       Label: Lecture
 21 |       Slides:
 22 |         - {Course Logistics and Overview: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/01a.course-logistics.pdf"}
 23 |         - {Statistical Learning Theory: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/01b.intro-stat-learning-theory.pdf"}
 24 | #        - {Stochastic Gradient Descent: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/01c.SGD.pdf"}
 25 |         - {Excess Risk Decomposition: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/01c.excess-risk-decomposition.pdf"}
 26 |       Notes:
 27 |         - {Conditional Expectations: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/conditional-expectations.pdf"}
 28 |         - {SLT Concept Check Questions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/1-Lec-Check.pdf"}
 29 |         - {SLT Concept Check Solutions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/1-Lec-Check_sol.pdf"}
 30 |       References:
 31 | #        - {"Bottou's SGD Tricks": "http://leon.bottou.org/papers/bottou-tricks-2012"}
 32 |     2019-01-30:
 33 |       Label: Lab
 34 |       Slides:
 35 |        - {Gradients and Directional Derivatives: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/1-gradients-Slides.pdf"}
 36 |       Notes:
 37 |        - {Gradients and Directional Derivatives: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/1-gradients-Notes_sol.pdf"}
 38 |        - {Gradient Concept Check Questions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/1-Lab-Check.pdf"}
 39 |        - {Gradient Concept Check Solutions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/1-Lab-Check_sol.pdf"}
 40 |        - {Directional Derivatives and Approximation (Short): "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/directional-derivative.pdf"}
 41 |       References:
 42 |        - {Barnes "Matrix Differentiation" notes: "http://www.atmos.washington.edu/~dennis/MatrixCalculus.pdf"}
 43 | -
 44 |   Title: Week 2
 45 |   Events:
 46 |     2019-02-05:
 47 |       Label: Lecture
 48 |       Slides:
 49 |        - {Stochastic Gradient Descent: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/02a.SGD.pdf"}
 50 |        - {Gradient Descent: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/02b.gradient-descent.pdf"}
 51 |        - {L1 and L2 regularization: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/02c.L1L2-regularization.pdf"}
 52 |       Notes:
 53 |        - {PreLecture Concept Check Questions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/2-PreLec-Check.pdf"}
 54 |        - {PreLecture Concept Check Solutions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/2-PreLec-Check_sol.pdf"}
 55 |        - {Completing the Square: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/completing-the-square.pdf"}
 56 |        - {'Excess Risk, Stochastic Gradient Descent and L1/L2 Questions': "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/2-Lec-Check.pdf"}
 57 |        - {'Excess Risk, Stochastic Gradient Descent and L1/L2 Solutions': "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/2-Lec-Check_sol.pdf"}
 58 |       References:
 59 |         - "HTF Ch. 3"
 60 |     2019-02-06:
 61 |       Label: Lab
 62 |       Slides:
 63 |        # - {Grouping and Elastic Net: "https://davidrosenberg.github.io/mlcourse/Archive/2017/Lectures/3a.elastic-net.pdf"}
 64 |        - {STL & Excess Risk Decomposition Demo Questions (ipynb) : "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/2-STL_Lab-Questions.ipynb"}
 65 |        - {STL & Excess Risk Decomposition Demo Answers (ipynb) : "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/2-STL_Lab-Answers.ipynb"}
 66 |       References:
 67 |         # - "HTF 3.4"
 68 | -
 69 |   Title: Week 3
 70 |   Events:
 71 |     2019-02-12:
 72 |       Label: Lecture
 73 |       Slides:
 74 |        - {Elastic Net and Correlation: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/03a.elastic-net.pdf"}
 75 |        - {Loss Functions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/03b.loss-functions.pdf"}
 76 |       Notes:
 77 |        - {Elastic Net correlation theorem: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/elastic-net-theorem.pdf"}
 78 |        - {Lasso and Elastic Net (ipynb): "https://github.com/davidrosenberg/mlcourse/blob/gh-pages/Notebooks/Lasso%20and%20Elastic%20Net/lasso_and_elastic_net.ipynb"}
 79 |       References:
 80 |        - "HTF 3.4"
 81 |        - {'Mairal, Bach, and Ponce on Sparse Modeling': "https://arxiv.org/pdf/1411.3230v2.pdf"}
 82 |     2019-02-13:
 83 |       Label: Lab
 84 |       Slides:
 85 |        - {Subgradient Descent: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/03c.subgradient-descent-lasso.pdf"}
 86 |       Notes:
 87 |        - {"Subgradients": "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/4-Subgradients-Notes_sol.pdf"}
 88 |        - {Subgradients and Lagrangian Duality Questions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/3-Lab-Check.pdf"}
 89 |        - {Subgradients and Lagrangian Duality Solutions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/3-Lab-Check_sol.pdf"}
 90 |       References:
 91 |        - {'Boyd subgradient notes': "http://web.stanford.edu/class/ee364b/lectures.html"}
 92 | 
 93 | 
 94 | -
 95 |   Title: Week 4
 96 |   Events:
 97 |     2019-02-19:
 98 |       Label: Lecture
 99 |       Slides:
100 |        - {Lagrangian Duality and Convex Optimization: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/04a.convex-optimization.pdf"}
101 |        - {Support Vector Machines: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/04b.SVM.pdf"}
102 |        - {SVM and Complementary Slackness: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/04c.SVM-ComplementarySlackness.pdf"}
103 |        - {Kernel Methods: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/04d.kernel-methods.pdf"}
104 | #        - {Lagrangian Duality in 10 Minutes (Optional)): "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/04d.lagrangian-duality-in-ten-minutes.pdf"}
105 |       Notes:
106 |        - {Pre-lecture warmup for SVM and Lagrangians: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/svm-lecture-prep.pdf"}
107 |        - {SVM Insights from Duality: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/SVM-main-points.pdf"}
108 | #        - {Subgradients and Lagrangian Duality Questions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/4-Lec-Check.pdf"}
109 | #        - {Subgradients and Lagrangian Duality Solutions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/4-Lec-Check_sol.pdf"}
110 |       References:
111 |        - {Support Vector Machines (Optional): "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/svm-notes.pdf"}
112 |        - {Extreme Abridgement of BV (Optional): "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/convex-optimization.pdf"}
113 |     2019-02-20:
114 |       Label: Lab
115 |       Slides:
116 |         - {Geometric Derivation of SVMs: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/3-SVM-Slides.pdf"}
117 | #        - {Features: "https://github.com/davidrosenberg/mlcourse/blob/gh-pages/Labs/4-Features/tex/04e.features.pdf"}
118 |       Notes:
119 |         - {Geometric Derivation of SVMs: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/3-SVM-Notes_sol.pdf"}
120 |         - {Uniqueness of SVM Solution: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/UniquenessOfSVM.pdf"}
121 | #        - {Simplest Example: "https://github.com/davidrosenberg/mlcourse/blob/gh-pages/Notebooks/Features/simple_feature_transformations.ipynb"}
122 | #        - {Ingesting text with BOW: "https://github.com/davidrosenberg/mlcourse/blob/gh-pages/Notebooks/Features/test_BOW.ipynb"}
123 | #        - {Polynomial features: "https://github.com/davidrosenberg/mlcourse/blob/gh-pages/Notebooks/Features/polynomial_feature_comparison.ipynb"}
124 | #        - {Vector quantization with k-means: "https://github.com/davidrosenberg/mlcourse/blob/gh-pages/Notebooks/Features/vector_quantization.ipynb"}
125 |       References:
126 | #        - {Feature Engineering for Machine Learning by Casari and Zheng: "https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/"}
127 | -
128 |   Title: Week 5
129 |   Events:
130 |     2019-02-26:
131 |       Label: Lecture
132 |       Slides:
133 |         - {The Representer Theorem and Kernelization: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/05a.representer-theorem.pdf"}
134 |         - {Kernel Methods: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/05b.kernel-methods.pdf"}
135 |       Notes:
136 |        - {Kernel Concept Check Questions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/5-Lab-Check.pdf"}
137 |        - {Kernel Concept Check Solutions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/5-Lab-Check_sol.pdf"}
138 |       References:
139 |         - "SSBD Chapter 16"
140 |         - {"A Survey of Kernels for Structured Data": "http://homepages.rpi.edu/~bennek/class/mmld/papers/p49-gartner.pdf"}
141 |     2019-02-27:
142 |       Label: Lab
143 |       Slides:
144 |         - {Kernel Methods Continued: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/5-kernel-methods-continued.pdf"}
145 | -
146 |   Title: Week 6
147 |   Events:
148 |     2019-03-05:
149 |       Label: Lecture
150 |       Slides:
151 |        - {CitySense: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/05b.citysense.pdf"}
152 |        - {Maximum Likelihood: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/05c.MLE.pdf"}
153 |        - {Conditional Probability Models: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/06a.conditional-probability-models.pdf"}
154 |       Notes:
155 |        - {"Exponential Distribution Example (First part)": "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/conditional-exponential-distributions.pdf"}
156 |        - {Conditional Model Questions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/10-Lab-Check.pdf"}
157 |        - {Conditional Model Solutions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/10-Lab-Check_sol.pdf"}
158 |     2019-03-06:
159 |       Label: Lab
160 |       Slides:
161 |        - {Review for Midterm: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/midterm-review.pdf"}
162 | -
163 |   Title: Week 7
164 |   Events:
165 |     2019-03-12:
166 |       Label: Midterm Exam
167 |     2019-03-13:
168 |       Label: Lab
169 |       Slides:
170 |        - {Review for MLE and Conditional Model: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/Review-MLE-Conditional-probability-densities.pdf"}
171 | -
172 |   Title: Week 8
173 |   Events:
174 |     2019-03-26:
175 |       Label: Lecture
176 |       Slides:
177 |        - {'Bayesian Methods': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/08b.bayesian-methods.pdf"}
178 |        - {'Bayesian Conditional Models': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/08a.bayesian-regression.pdf"}
179 |       Notes:
180 |        - {"Proportionality Review": "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/proportionality.pdf"}
181 |        - {"Thompson Sampling for Bernoulli Bandits [Optional]": "https://davidrosenberg.github.io/mlcourse/in-prep/thompson-sampling-bernoulli.pdf"}
182 |        - {"Multivariate Gaussians (draft)": "https://davidrosenberg.github.io/mlcourse/in-prep/multivariate-gaussian.pdf"}
183 |        - {"Bayesian Linear Regression (draft)": "https://davidrosenberg.github.io/mlcourse/in-prep/bayesian-regression.pdf"}
184 |        - {Bayesian Methods and Regression Questions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/11-Lec-Check.pdf"}
185 |        - {Bayesian Methods and Regression Solutions: "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/11-Lec-Check_sol.pdf"}
186 |       References:
187 |        - "Barber 9.1, 18.1"
188 |        - "Bishop 3.3"
189 |     2019-03-27:
190 |       Label: Midterm Solution Discussion
191 | -
192 |   Title: Week 9
193 |   Events:
194 |     2019-04-02:
195 |       Label: Lecture
196 |       Slides:
197 |        - {'Multiclass': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/09b.multiclass.pdf"}
198 |        # - {'Classification and Regression Trees': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/10a.trees.pdf"}
199 |       Notes:
200 |        - {"Multiclass Questions": "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/7-Lec-Check.pdf"}
201 |        - {"Multiclass Solutions": "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/7-Lec-Check_sol.pdf"}
202 | #        - {"Proportionality Review": "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/proportionality.pdf"}
203 | #        - {"Multivariate Gaussians (draft)": "https://davidrosenberg.github.io/mlcourse/in-prep/multivariate-gaussian.pdf"}
204 | #        - {"Bayesian Linear Regression (draft)": "https://davidrosenberg.github.io/mlcourse/in-prep/bayesian-regression.pdf"}
205 | 
206 |       References:
207 |        - "SSBD Chapter 17"
208 |        - {In Defense of One-Vs-All Classification: "http://www.jmlr.org/papers/v5/rifkin04a.html"}
209 |        - {Reducing Multiclass to Binary: "http://www.jmlr.org/papers/volume1/allwein00a/allwein00a.pdf"}
210 |        # - "JWHT 8.1 (Trees)"
211 |        # - "HTF 9.2 (Trees)"
212 | 
213 |     2019-04-03:
214 |       Label: Lab
215 |       Slides:
216 |        - {'Structured-Prediction-and-Bayesian-Recap': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/structured_prediction_bayesian_recap.pdf"}
217 |       Notes:
218 | #        - {"Multiclass Questions": "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/7-Lec-Check.pdf"}
219 | #        - {"Multiclass Solutions": "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/7-Lec-Check_sol.pdf"}
220 |       References:
221 |        # - "SSBD Chapter 17"
222 | #        - {In Defense of One-Vs-All Classification: "http://www.jmlr.org/papers/v5/rifkin04a.html"}
223 | #        - {Reducing Multiclass to Binary: "http://www.jmlr.org/papers/volume1/allwein00a/allwein00a.pdf"}
224 | -
225 |   Title: Week 10
226 |   Events:
227 |     2019-04-09:
228 |       Label: Lecture
229 |       Slides:
230 |        - {'Classification and Regression Trees': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/10a.trees.pdf"}
231 |       References:
232 |        - "JWHT 8.1 (Trees)"
233 |        - "HTF 9.2 (Trees)"
234 |     2019-04-10:
235 |       Label: Lab
236 |       Slides:
237 |        - {'Multiclass and Trees Questions': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/Multiclass_trees_week10.pdf"}
238 |       References:
239 | #        - "JWHT 5.2 (Bootstrap)"
240 | #        - "HTF 7.11 (Bootstrap)"
241 | -
242 |   Title: Week 11
243 |   Events:
244 |     2019-04-16:
245 |       Label: Lecture
246 |       Slides:
247 |         - {'Intro to the Bootstrap': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/11a.bootstrap.pdf"}
248 |         - {'Bagging and Random Forests': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/11b.bagging-random-forests.pdf"}
249 |         - {'Adaboost': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/11c.adaboost.pdf"}
250 |       Notes:
251 |        - {"Trees, Bootstrap, Bagging, and RF Questions": "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/9-Lec-Check.pdf"}
252 |        - {"Trees, Bootstrap, Bagging, and RF Solutions": "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/9-Lec-Check_sol.pdf"}
253 |       References:
254 |         - "JWHT 5.2 (Bootstrap)"
255 |         - "HTF 7.11 (Bootstrap)"
256 |         - "JWHT 8.2 (Bagging/RF)"
257 |         - "HTF 8.7, 15, 10 (Bagging/RF)"
258 |         - {A Conversation with Jerry Friedman: "https://arxiv.org/pdf/1507.08502.pdf"}
259 |     2019-04-17:
260 |       Label: Lab
261 |       Slides:
262 |        - {'Bagging and Boosting Questions': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/bootstrap_bagging_boosting.pdf"}
263 |       Notes:
264 | #        - {"Gradient Boosting": "https://davidrosenberg.github.io/mlcourse/Archive/2017/Labs/9-GBM-Notes.pdf"}
265 | #        - {"Boosting Questions": "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/boosting-Lec-Check.pdf"}
266 | #        - {"Boosting Solutions": "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/boosting-Lec-Check_sol.pdf"}
267 | #        - {"gbm.py": "https://davidrosenberg.github.io/mlcourse/Archive/2017/Labs/gbm.py"}
268 | #        - {"Exponential Distribution Gradient Boosting": "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/conditional-exponential-distributions.pdf"}
269 | #        - {"Poisson Gradient Boosting": "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/poisson-gradient-boosting.pdf"}
270 |       References:
271 | #        - {Friedman's GBM Paper: http://statweb.stanford.edu/~jhf/ftp/trebst.pdf}
272 | #        - {Ridgeway's GBM Guide: http://www.saedsayad.com/docs/gbm2.pdf}
273 | #        - {XGBoost Paper: http://arxiv.org/abs/1603.02754}
274 | #        - {"Bühlmann and Hothorn's Boosting Paper": https://projecteuclid.org/euclid.ss/1207580163}
275 | -
276 |   Title: Week 12
277 |   Events:
278 |     2019-04-23:
279 |       Label: Lecture
280 |       Slides:
281 |        - {'Gradient Boosting': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/11b.gradient-boosting.pdf"}
282 |        - {'Gradient Boosting Lab': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/GBM_Lecture.pdf"}
283 |        # - {'Neural Networks': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/12a.neural-networks.pdf"}
284 | #        - {'Back Propagation': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/12b.backpropagation.pdf"}
285 |       Notes:
286 |        - {"Gradient Boosting": "https://davidrosenberg.github.io/mlcourse/Archive/2017/Labs/9-GBM-Notes.pdf"}
287 |        - {"gbm.py": "https://davidrosenberg.github.io/mlcourse/Archive/2017/Labs/gbm.py"}
288 |        - {"Exponential Distribution Gradient Boosting": "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/conditional-exponential-distributions.pdf"}
289 |        - {"Poisson Gradient Boosting": "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/poisson-gradient-boosting.pdf"}
290 |       References:
291 |        - {Friedman's GBM Paper: http://statweb.stanford.edu/~jhf/ftp/trebst.pdf}
292 |        - {Ridgeway's GBM Guide: http://www.saedsayad.com/docs/gbm2.pdf}
293 |        - {XGBoost Paper: http://arxiv.org/abs/1603.02754}
294 |        - {"Bühlmann and Hothorn's Boosting Paper": https://projecteuclid.org/euclid.ss/1207580163}
295 | #        - {'Yes you should understand backprop (Karpathy)': "https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b"}
296 | #        - {'Challenges with backprop (Karpathy Lecture)': "https://youtu.be/gYpoJMlgyXA?t=13m44s"}
297 |     2019-04-24:
298 |       Label: Lab
299 |       Slides:
300 |        - {'Neural Networks': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/12a.neural-networks.pdf"}
301 |       Notes:
302 |        # - {"Gradient Boosting": "https://davidrosenberg.github.io/mlcourse/Archive/2017/Labs/9-GBM-Notes.pdf"}
303 | #        - {"Boosting Questions": "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/boosting-Lec-Check.pdf"}
304 | #        - {"Boosting Solutions": "https://davidrosenberg.github.io/mlcourse/Archive/2019/ConceptChecks/boosting-Lec-Check_sol.pdf"}
305 | #        - {"gbm.py": "https://davidrosenberg.github.io/mlcourse/Archive/2017/Labs/gbm.py"}
306 |        # - {"Exponential Distribution Gradient Boosting": "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/conditional-exponential-distributions.pdf"}
307 |        # - {"Poisson Gradient Boosting": "https://davidrosenberg.github.io/mlcourse/Archive/2019/Notes/poisson-gradient-boosting.pdf"}
308 |       References:
309 | #        - {Friedman's GBM Paper: http://statweb.stanford.edu/~jhf/ftp/trebst.pdf}
310 | #        - {Ridgeway's GBM Guide: http://www.saedsayad.com/docs/gbm2.pdf}
311 | #        - {XGBoost Paper: http://arxiv.org/abs/1603.02754}
312 | #        - {"Bühlmann and Hothorn's Boosting Paper": https://projecteuclid.org/euclid.ss/1207580163}
313 |        - {"Michael Nielsen's book on Neural Nets": "http://neuralnetworksanddeeplearning.com/"}
314 | -
315 |   Title: Week 13
316 |   Events:
317 |     2018-04-30:
318 |       Label: Lecture
319 | #      Video: "https://youtu.be/tgCIAft_W24?t=48m48s"
320 |       Slides:
321 |        - {'Back Propagation': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/12b.backpropagation.pdf"}
322 |        - {'k-Means': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/13a.k-means.pdf"}
323 | #        - {'Gaussian Mixture Models': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/13b.mixture-models.pdf"}
324 | #        - {'General EM Algorithm': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/13c.EM-algorithm.pdf"}
325 |       Notes:
326 |       References:
327 |        - {'Yes you should understand backprop (Karpathy)': "https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b"}
328 |        - {'Challenges with backprop (Karpathy Lecture)': "https://youtu.be/gYpoJMlgyXA?t=13m44s"}
329 |        - {"Michael Nielsen: How the backpropagation algorithm works": "http://neuralnetworksanddeeplearning.com/chap2.html"}
330 |        - "HTF, 13.2.1 (k-means)"
331 |         # - "Bishop 9.2,9.3 (GMM/EM)"
332 | #        - {"An Alternative to EM for GMM [Optional]": "https://arxiv.org/pdf/1706.03267.pdf"}
333 |     2019-05-01:
334 |       Label: Lab
335 |       Slides:
336 |        - {'Neural Networks Questions': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/neural-network-questions.pdf"}
337 | -
338 |   Title: Week 14
339 |   Events:
340 |     2019-05-07:
341 |       Label: Lecture
342 |       Slides:
343 |        - {'Gaussian Mixture Models': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/13b.mixture-models.pdf"}
344 |        - {'General EM Algorithm': "https://davidrosenberg.github.io/mlcourse/Archive/2019/Lectures/13c.EM-algorithm.pdf"}
345 |       Notes:
346 |       References:
347 |        - "Bishop 9.2,9.3 (GMM/EM)"
348 |        - {"An Alternative to EM for GMM [Optional]": "https://arxiv.org/pdf/1706.03267.pdf"}
349 |     2018-05-08:
350 |       Label: Course Review
351 |       Slides:
352 |        - {Review for Final: "https://davidrosenberg.github.io/mlcourse/Archive/2019/Labs/ML_Final_Review.pdf"}
353 | 
354 | 


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1 | When this-week.yml has the string "null" in it, it's not included at the top
2 | 


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2 | 


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/deploy.sh:
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 1 | #!/bin/bash
 2 | ## Run this from project root
 3 | 
 4 | set -e # Exit with nonzero exit code if anything fails
 5 | 
 6 | SOURCE_BRANCH="master"
 7 | TARGET_BRANCH="gh-pages"
 8 | 
 9 | function doCompile {
10 |     ./build.sh
11 | }
12 | 
13 | # Save some useful information
14 | REPO=`git config remote.origin.url`
15 | SSH_REPO=${REPO/https:\/\/github.com\//git@github.com:}
16 | SHA=`git rev-parse --verify HEAD`
17 | 
18 | # Clone the existing gh-pages for this repo into out/
19 | # Create a new empty branch if gh-pages doesn't exist yet (should only happen on first deply)
20 | rm -rf out
21 | git clone $REPO out
22 | cd out
23 | git checkout $TARGET_BRANCH || git checkout --orphan $TARGET_BRANCH
24 | cd ..
25 | 
26 | # Clean out existing contents
27 | rm -rf out/**/* || exit 0
28 | 
29 | # Run our compile script
30 | doCompile
31 | 
32 | # Now let's go have some fun with the cloned repo
33 | cd out
34 | 
35 | # Commit the "changes", i.e. the new version.
36 | # The delta will show diffs between new and old versions.
37 | git add --all .
38 | git commit -m "Deploy to GitHub Pages: ${SHA}"
39 | 
40 | # Now that we're all set up, we can push.
41 | git push $REPO $TARGET_BRANCH
42 | 


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  1 | <!DOCTYPE html>
  2 | <html lang="en">
  3 | <meta charset="utf-8">
  4 | <title>DS-GA 1003 / CSCI-GA 2567: Machine Learning, Spring 2019</title>
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 10 | <link rel="canonical" href="https://davidrosenberg.github.io/ml2019">
 11 | 
 12 | <nav>
 13 |     <a href="#home">Home</a>
 14 |     <a href="#about">About</a>
 15 |     <a href="#resources">Resources</a>
 16 |     <a href="#lectures">Lectures</a>
 17 |     <a href="#assignments">Assignments</a>
 18 |     <a href="#people">People</a>
 19 | </nav>
 20 | 
 21 | <section id="home">
 22 |     <h1>
 23 |         Machine Learning
 24 |         <span class="course">
 25 |             DS-GA 1003 · Spring 2019 ·
 26 |             <a class="department" href="http://cds.nyu.edu/">NYU Center for Data Science</a>
 27 |         </span>
 28 |     </h1>
 29 | 
 30 |     <table id="course-info">
 31 |         <tr>
 32 |             <th>Lead Instructor</th>
 33 |             <td>Julia Kempe<a class="icon email" 
 34 | href="mailto:jk185@nyu.edu"></a></td>
 35 |         </tr>
 36 | 
 37 |         <tr>
 38 |             <th>Co-Instructor</th>
 39 |             <td>David Rosenberg <a class="icon email" href="mailto:dr129@nyu.edu"></a></td>
 40 |         </tr>
 41 |         <tr>
 42 |             <th>Lecture</th>
 43 | 		        <td>Tuesday 5:20pm–7pm, <a href="http://4sq.com/816YxF">MEYER</a> 121 (<a href="https://goo.gl/maps/75R9D6qb5An">4 Washington Pl</a>)</td>
 44 |         </tr>
 45 |         <tr>
 46 |             <th>Lab</th>
 47 | 		        <td>Wednesday 6:45pm–7:35pm, <a href="http://4sq.com/816YxF">MEYER</a> 121 (<a href="https://goo.gl/maps/75R9D6qb5An">4 Washington Pl</a>)</td>
 48 |         </tr>
 49 |         <tr>
 50 |             <th rowspan="3">Office Hours</th>
 51 |             <td>Instructor: Tuesdays 4:00-5:00pm <a href="https://goo.gl/VoxR4R">CDS</a> (<a href="https://goo.gl/uU2EwV">60 5th Ave.</a>), 6th floor, Room 620</td>
 52 |         </tr>
 53 |         <tr>
 54 |             <td>Section Leaders: Wednesdays 8:00-9:00pm, <a href="https://goo.gl/VoxR4R">CDS</a> (<a href="https://goo.gl/uU2EwV">60 5th Ave.</a>) Room C-15</td>
 55 |         </tr>
 56 |         <tr>
 57 |             <td>Graders: Wed 1:30-2:30pm, Thu 12:30-1:30pm <a href="https://goo.gl/VoxR4R">CDS</a> (<a href="https://goo.gl/uU2EwV">60 5th Ave.</a>), Room 667</td>
 58 |         </tr>
 59 |         <tr>
 60 |             <th rowspan="2">Note</th>
 61 | 		        <td>In Weeks 5, 7 and 11 David Rosenberg will teach and hold office hours - </td>
 62 |         </tr>
 63 |         <tr>
 64 | <td>perhaps not at the usual time or place. We will announce these changes. </td>
 65 |         </tr>
 66 | 
 67 |     </table>
 68 | 
 69 |     {{> this-week thisWeek }}
 70 | </section>
 71 | <section id="about">
 72 |     <h1>About This Course</h1>
 73 | 
 74 |     <div class="module">
 75 |         <p>This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice.  This course also serves as a foundation on which more specialized courses and further independent study can build. A tentative syllabus can be found <a href="https://davidrosenberg.github.io/mlcourse/syllabusDS-GA1003-Spring2019.pdf">here</a>.</p>
 76 | 
 77 |         <p>This course was designed as part of the core curriculum for the Center for Data Science's <a href="http://cds.nyu.edu/academics/ms-in-data-science/">Masters degree in Data Science</a>. Other interested students who satisfy the prerequisites are welcome to take the class as well.  This class is intended as a continuation of <a href="http://cds.nyu.edu/course-pages/ds-ga-1001-intro-data-science/">DS-GA-1001 Intro to Data Science</a>, which covers some important, fundamental data science topics that may not be explicitly covered in this DS-GA class (e.g. data cleaning, cross-validation, and sampling bias).</p>
 78 | 
 79 |         <p>We will use <a href="https://piazza.com/">Piazza</a> for class discussion. Rather than emailing questions to the teaching staff, please <a href="https://piazza.com/nyu/spring2019/dsga1003/home">post your questions on Piazza</a>, where they will be answered by the instructor, TAs, graders, and other students.  For questions that are not specific to the class, you are also encouraged to post to <a href="http://stackoverflow.com/">Stack Overflow</a> for programming questions and <a href="http://stats.stackexchange.com/">Cross Validated</a> for statistics and machine learning questions.  Please also post a link to these postings in Piazza, so others in the class can answer the questions and benefit from the answers.
 80 | 
 81 |         <!-- Without registering, you can also view an <a href="https://piazza.com/class/i2jg9qgaxwr5fq?cid=14">anonymized version of our Piazza board</a>.</p> -->
 82 | 
 83 |         <p>Other information:</p>
 84 |         <ul>
 85 |             <!-- -  <li>Course details can be found in the <a href="https://davidrosenberg.github.io/mlcourse/syllabusDS-GA1003-Spring2017.pdf">syllabus</a>.</li>   -->          
 86 |     	    <!--                 <li> Video recordings of lectures can be found at <a href="http://techtalks.tv/machine_learning_spring_2016/">http://techtalks.tv/machine_learning_spring_2016/</a>. -->
 87 |             <!--             <li>The <a href="https://www.google.com/calendar/embed?src=q5os9dtr9kebkkvv17lqtqj5qc%40group.calendar.google.com&ctz=America/New_York">Course Calendar</a> contains all class meeting dates.</li> -->
 88 |             <li>All course materials are stored in a <a href="https://github.com/davidrosenberg/mlcourse">GitHub repository</a>.  Check the repository to see when something was last updated.</li>
 89 |             <li>For registration information, please contact <a href="mailto:kangeles@nyu.edu">Kathryn Angeles</a>.</li>
 90 |             <li><em>The course conforms to <a href="http://www.nyu.edu/about/policies-guidelines-compliance/policies-and-guidelines/academic-integrity-for-students-at-nyu.html">NYU’s policy on academic integrity for students</a>.</em></li>
 91 |         </ul>
 92 |     </div>
 93 | 
 94 |     <section>
 95 |         <h1>Prerequisites</h1>
 96 |         <ul>
 97 |             <li><a href="https://github.com/briandalessandro/DataScienceCourse/blob/master/ipython/references/Syllabus_2017.pdf"><strong>DS-GA-1001: Intro to Data Science</strong></a> or its equivalent</li>
 98 |             <li><a href="http://www.cims.nyu.edu/~cfgranda/pages/DSGA1002_fall15/index.html"><strong>DS-GA-1002: Statistical and Mathematical Methods</strong></a> or its equivalent</li>
 99 |             <li><strong>Solid mathematical background</strong>, equivalent to a 1-semester undergraduate course in each of the following: linear algebra, multivariate calculus (primarily differential calculus), probability theory, and statistics.  (The coverage in the 2015 version of DS-GA 1002, linked above, is sufficient.)</li>
100 |             <li><strong>Python programming required</strong> for most homework assignments.</li>
101 |             <li><em>Recommended:</em> Computer science background up to a "data structures and algorithms" course</li>
102 |             <li><em>Recommended:</em> At least one advanced, proof-based mathematics course</li>
103 |             <li>Some prerequisites may be waived with permission of the instructor</li>
104 |             <li>You can also self-assess your preparation by filling out the <a href="https://davidrosenberg.github.io/mlcourse/Notes/prereq-questions/prereq-self-assessment.pdf">Prerequisite Questionnaire</a></li>
105 |         </ul>
106 |     </section>
107 |     <section>
108 |         <h1>Grading</h1>
109 |         <p><strong>Homework (40%) + Midterm Exam (30%) + Final Exam (30%)</strong></p>
110 |         <p>
111 |             Many homework assignments will have problems designated as “optional”.  At the end of the semester, strong performance on these problems may lift the final course grade by up to half a letter grade (e.g. B+ to A-  or A- to A), especially for borderline grades.  You should view the optional problems primarily as a way to engage with more material, if you have the time.  Along with the performance on optional problems, we will also consider significant contributions to Piazza and in-class discussions for boosting a borderline grade.
112 |         </p>
113 |     </section>
114 | 
115 |     <section>
116 |         <h1>Important Dates</h1>
117 |         <ul>
118 |             <li><strong>Midterm Exam</strong> (100 min) Tuesday, March 12th, 5:20–7pm.</li>
119 |             <li><strong>Final Exam</strong> (100 min) Thursday, May 16th, 6-7:50pm (confirmed).</li>
120 |             <li>See <a href="#assignments">Assignments</a> section for homework-related deadlines.</li>
121 |         </ul>
122 |     </section>
123 | 
124 | 
125 | </section>
126 | 
127 | <section id="resources">
128 |     <h1>Resources</h1>
129 | 
130 |     <section id="textbooks">
131 |         <h1>Textbooks</h1>
132 | 
133 |         <a href="https://web.stanford.edu/~hastie/ElemStatLearn/"><img src="images/hastie-1x.png" srcset="images/hastie-1x.png 1x, images/hastie-2x.jpg 2x, images/hastie-3x.jpg 3x" alt="The cover of Elements of Statistical Learning"></a>
134 | 
135 |         <a href="http://www-bcf.usc.edu/~gareth/ISL/"><img src="images/james-1x.jpg" srcset="images/james-1x.jpg 1x, images/james-2x.jpg 2x, images/james-3x.jpg 3x" alt="The cover of An Introduction to Statistical Learning"></a>
136 | 
137 |         <a href="http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/"><img src="images/shalev-shwartz-original.jpg" alt="The cover of Understanding Machine Learning: From Theory to Algorithms"></a>
138 | 
139 |         <a href="http://a.co/77AlDxk"><img src="images/bishop-1x.jpg" srcset="images/bishop-1x.jpg 1x, images/bishop-2x.jpg 2x, images/bishop-3x.jpg 3x" alt="The cover of Pattern Recognition and Machine Learning"></a>
140 | 
141 |         <a href="http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage"><img src="images/barber-1x.jpg" srcset="images/barber-1x.jpg 1x, images/barber-2x.jpg 2x, images/barber-3x.jpg 3x" alt="The cover of Bayesian Reasoning and Machine Learning"></a>
142 | 
143 |         <dl>
144 | 
145 |             <dt><a href="http://statweb.stanford.edu/~hastie/ElemStatLearn/"><cite>The
146 |             Elements of Statistical Learning</cite> (Hastie, Friedman, and Tibshirani)</a>
147 |             <dd>This will be our main textbook for L1 and L2 regularization, trees, bagging, random forests, and boosting.  It's written by three statisticians who invented many of the techniques discussed. There's an easier version of this book that covers many of the same topics, described below. (Available for free as a PDF.)
148 | 
149 |             <dt><a href="http://www-bcf.usc.edu/~gareth/ISL/"><cite>An Introduction to Statistical Learning</cite> (James, Witten, Hastie, and Tibshirani)</a>
150 |             <dd>This book is written by two of the same authors as The Elements of Statistical Learning. It's much less intense mathematically, and it's good for a lighter introduction to the topics. (Available for free as a PDF.)
151 | 
152 |     	    <dt><a href="http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning"><cite>Understanding Machine Learning: From Theory to Algorithms</cite> (Shalev-Shwartz and Ben-David)</a>
153 |     	    <dd>Last year this was our primary reference for kernel methods and multiclass classification, and we may use it even more this year.  Covers a lot of theory that we don't go into, but it would be a good supplemental resource for a more theoretical course, such as Mohri's <a href="http://www.cs.nyu.edu/~mohri/ml17/">Foundations of Machine Learning</a> course. (Available for free as a PDF.)
154 | 
155 |               <dt><a href="http://a.co/77AlDxk"><cite>Pattern Recognition and Machine Learning</cite> (Christopher Bishop)</a>
156 |     	    <dd>Our primary reference for probabilistic methods, including bayesian regression, latent variable models, and the EM algorithm.  It's highly recommended, but unfortunately not free online.
157 | 
158 |     	    <dt><a href="http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage"><cite>Bayesian Reasoning and Machine Learning</cite> (David Barber)</a>
159 |     	    <dd>A very nice resource for our topics in probabilistic modeling, and a possible substitute for the Bishop book.  Would serve as a good supplemental reference for a more advanced course in probabilistic modeling, such as <a href="https://inf16nyu.github.io/home/">DS-GA 1005: Inference and Representation</a> (Available for free as a PDF.)
160 |         </dl>
161 | 
162 |         <dt><a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/"><cite>Hands-On Machine Learning with Scikit-Learn and TensorFlow</cite> (Aurélien Géron)</a>
163 |             <dd>This is a practical guide to machine learning that corresponds fairly well with the content and level of our course.  While most of our homework is about coding ML from scratch with numpy, this book makes heavy use of scikit-learn and TensorFlow. Comfort with the first two chapters of this book would be part of the ideal preparation for this course, and it will also be a handy reference for practical projects and work beyond this course, when you'll want to make use of existing ML packages, rather than rolling your own.</dd>
164 | 
165 |     	      <dt><a href="http://data-science-for-biz.com/"><cite>Data Science for Business</cite> (Provost and Fawcett)</a>
166 |     	          <dd>Ideally, this would be everybody's first book on machine learning.  The intended audience is both the ML practitioner and the ML product manager.  It's full of important core concepts and practical wisdom.  The math is so minimal that it's perfect for reading on your phone, and I encourage you to read it in parallel to doing this class, especially if you haven't taken DS-GA 1001.</dd>
167 |             </dt>
168 | 
169 | 
170 |     </section>
171 | 
172 |     <section id="references">
173 |         <h1>Other tutorials and references</h1>
174 | 
175 |         <ul>
176 |             <li><a href="http://www.cims.nyu.edu/~cfgranda/pages/DSGA1002_fall15/notes.html">Carlos Fernandez-Granda's lecture notes</a> provide a comprehensive review of the prerequisite material in linear algebra, probability, statistics, and optimization.
177 |     	    <li><a href="http://nbviewer.ipython.org/github/briandalessandro/DataScienceCourse/tree/master/ipython/">Brian Dalessandro's iPython notebooks</a> from <a href="https://github.com/briandalessandro/DataScienceCourse/blob/master/ipython/references/Syllabus_2017.pdf"><strong>DS-GA-1001: Intro to Data Science</strong></a>
178 |             <li><a href="http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=3274">The Matrix Cookbook</a> has lots of facts and identities about matrices and certain probability distributions.
179 |             <li><a href="http://cs229.stanford.edu/section/cs229-prob.pdf">Stanford CS229: "Review of Probability Theory"</a>
180 |             <li><a href="http://cs229.stanford.edu/section/cs229-linalg.pdf">Stanford CS229: "Linear Algebra Review and Reference"</a>
181 |             <li><a href="http://www.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf">Math for Machine Learning</a> by Hal Daumé III
182 |         </ul>
183 |     </section>
184 | 
185 |     <section id="software">
186 |         <h1>Software</h1>
187 | 
188 |         <ul>
189 |             <li><a href="http://www.numpy.org/">NumPy</a> is "the fundamental package for scientific computing with Python." Our homework assignments will use NumPy arrays extensively.
190 |             <li><a href="http://scikit-learn.org/stable/">scikit-learn</a> is a comprehensive machine learning toolkit for Python. We won't use this for most of the homework assignments, since we'll be coding things from scratch. However, you may want to run the scikit-learn version of the algorithms to check that your own outputs are correct. Also, studying the source code can be a good learning experience.
191 |         </ul>
192 |     </section>
193 | </section>
194 | 
195 | <section id="lectures">
196 |     <h1>Lectures</h1>
197 | 
198 |     <ul class="abbreviations">
199 |         <li> (HTF) refers to Hastie, Tibshirani, and Friedman's book <a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/"><cite>The Elements of Statistical Learning</cite></a>
200 |         <li> (SSBD) refers to Shalev-Shwartz and Ben-David's book <a href="http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/index.html/"><cite>Understanding Machine Learning: From Theory to Algorithms</cite></a>
201 |         <li> (JWHT) refers to James, Witten, Hastie, and Tibshirani's book <a href="http://www-bcf.usc.edu/~gareth/ISL"><cite>An Introduction to Statistical Learning</cite></a>
202 |     </ul>
203 | 
204 |     {{> lectures lectures }}
205 | </section>
206 | <section id="assignments">
207 |     <h1>Assignments</h1>
208 | 
209 |     <div class="policies">
210 |         <p><strong>Late Policy:</strong> Homeworks are due at {{assignmentsFrontmatter.[Due time]}} on the date specified.  Homeworks will still be accepted for 48 hours after this time but will have a 20% penalty.</p>
211 | 
212 |         <p><strong>Collaboration Policy:</strong> You may discuss problems with your classmates. However, you must write up the homework solutions and the code from scratch, without referring to notes from your joint session.  In your solution to each problem, you must write down the names of any person with whom you discussed the problem—this will not affect your grade.</p>
213 | 
214 |         <p><strong>Homework Submission:</strong> Homework should be submitted through <a href="https://gradescope.com">Gradescope</a>.  If you have not used Gradescope before, please watch this short video: <a href="https://gradescope.com/get_started">"For students: submitting homework."</a>  At the beginning of the semester, you will be added to the Gradescope class roster. This will give you access to the course page, and the assignment submission form. To submit assignments, you will need to:</p>
215 |         <ol>
216 |             <li> Upload a single PDF document containing all the math, code, plots, and exposition required for each problem.</li>
217 |             <li> Where homework assignments are divided into sections, <strong>please begin each section on a new page</strong>.</li>
218 |             <li> You will then select the appropriate page ranges for each homework problem, as described in the "submitting homework" video.</li>
219 |         </ol>
220 |         <p><strong>Homework Feedback:</strong> Check <a href="https://gradescope.com">Gradescope</a> to get your scores on each individual problem, as well as comments on your answers. Since Gradescope cannot distinguish between required and optional problems, final homework scores, separated into required and optional parts, will be posted on  <a href="https://newclasses.nyu.edu/portal">NYUClasses</a>.</p>
221 |     </div>
222 | 
223 |     {{> assignments assignments }}
224 | </section>
225 | 
226 | <section id="people">
227 |     <h1>People</h1>
228 | 
229 |     <section class="multiple-people">
230 |         <h1>Instructors</h1>
231 | 
232 | 	<ul>
233 |         <div class="person module instructor">
234 |             <img src="images/people/julia.jpg" alt="A photo of Julia Kempe">
235 |             <div class="info">
236 |                 <p class="name"><a href="http://www.cims.nyu.edu/~kempe">Julia Kempe</a></p>
237 |                 <p class="email"><a href="mailto:jk185@nyu.edu">jk185@nyu.edu</a></p>
238 |                 <p class="bio">Julia is the Director of the NYU <a href="http://www.cds.nyu.edu">Center for Data Science</a> (CDS). She is a professor of Computer Science and Mathematics at CDS and the NYU Courant Institute. </p>
239 |             </div>
240 |         </div>
241 |         <div class="person module instructor">
242 |             <img src="images/people/david.jpg" alt="A photo of David Rosenberg">
243 |             <div class="info">
244 |                 <p class="name"><a href="http://www.linkedin.com/pub/david-rosenberg/4/241/598">David Rosenberg</a></p>
245 |                 <p class="email"><a href="mailto:dr129@nyu.edu">dr129@nyu.edu</a></p>
246 |                 <p class="bio">David is a data scientist in the office of the CTO at <a href="http://www.bloomberglabs.com/data-science/">Bloomberg L.P.</a> Formerly he was Chief Scientist of <a href="http://national.yp.com/mobile/labs/">YP Mobile Labs</a> at <a href="http://www.yellowpages.com">YP</a>. </p>
247 |             </div>
248 |         </div>
249 | 	</ul>
250 | 
251 |     </section>
252 | 
253 |     <section>
254 |         <h1>Section Leaders</h1>
255 | 
256 | 	<ul>	
257 |         <div class="person module instructor">
258 |             <img src="images/people/sreyas.png" alt="A photo of Sreyas Mohan">
259 |             <div class="info">
260 |                 <p class="name"><a href="">Sreyas Mohan </a>(Head&nbsp;TA)</p>
261 | 		<p class="email"><a href="mailto:sm7582@nyu.edu">sm7582@nyu.edu</a></p>
262 |                 <p class="bio">Sreyas is a second year PhD student in the Data Science Program at CDS working with Prof. Carlos Fernandez-Granda and Prof. Eero Simoncelli. 
263 |  </p>
264 |             </div>
265 |         </div>
266 |         <div class="person module instructor">
267 |             <img src="images/people/xintian.jpeg" alt="A photo of Xintian Han">
268 |             <div class="info">
269 |                 <p class="name"><a href="">Xintian Han</a></p>
270 | 		<p class="email"><a href="mailto:xh1007@nyu.edu">xh1007@nyu.edu</a></p>
271 |                 <p class="bio">Xintian is a second year PhD student in the Data Science Program at CDS working with Prof. Rajesh Ranganath.</p>
272 |             </div>
273 |         </div>
274 | 	</ul>
275 |     </section>
276 |     <section class="multiple-people">
277 |         <h1>Graders</h1>
278 | 
279 |         <ul>
280 |             <li class="person module">
281 |                 <img src="images/people/sanyam.jpg" alt="A photo of Sanyam Kapur">
282 |                 <div class="info">
283 |                     <p class="name"><a href="">Sanyam Kapur </a> (Head&nbsp;Grader)</p>
284 |                     <p class="email"><a href="mailto:sanyam@nyu.edu">sanyam@nyu.edu</a></p>
285 |                     <p class="bio">Sanyam is a Masters Student in Computer Science at NYU Courant. He is currently working towards improving Markov Chain Monte Carlo methods.</p>
286 |                 </div>
287 |             </li>
288 |             <li class="person module">
289 |                 <img src="images/people/aakash.JPG" alt="A photo of Aakash Kaku">
290 |                 <div class="info">
291 |                     <p class="name"><a href="">Aakash Kaku</a></p>
292 |                     <p class="bio">Aakash is a second-year Masters student in the Data Science program at NYU. He is interested in solving problems in the healthcare domain using machine learning.</p>
293 |                 </div>
294 |             </li>
295 |             <li class="person module">
296 |                 <img src="images/people/mingsi.jpg" alt="A photo of Mingsi Long">
297 |                 <div class="info">
298 |                     <p class="name"><a href="">Mingsi Long</a></p>
299 |                     <p class="bio">Mingsi is a second year student in the Data Science Program at NYU CDS.</p>
300 |                 </div>
301 |             </li>
302 | 
303 |             <li class="person module">
304 |                 <img src="images/people/mihir.jpg" alt="A photo of Mihir Rana">
305 |                 <div class="info">
306 |                     <p class="name"><a href="">Mihir Rana</a></p>
307 |                     <p class="bio">Mihir is a Master's student in Data Science at the NYU Center for Data Science, interested in computer vision, reinforcement learning, and natural language understanding.</p>
308 |                 </div>
309 |             </li>
310 |             <li class="person module">
311 |                 <img src="images/people/tingyan.jpeg" alt="A photo of Tingyan Xiang">
312 |                 <div class="info">
313 |                     <p class="name"><a href="">Tingyan Xiang</a></p>
314 |                     <p class="bio">Tingyan is a second-year Masters student in the Data Science program at NYU.</p>
315 |                 </div>
316 |             </li>
317 |             <li class="person module">
318 |                 <img src="images/people/yi.jpg" alt="A photo of Yi Zhou">
319 |                 <div class="info">
320 |                     <p class="name"><a href="">Yi Zhou</a></p>
321 |                     <p class="bio">Yi is a second year student at the CS department at NYU Tandon.</p>
322 |                 </div>
323 |             </li>
324 |         </ul>
325 |     </section>
326 | 
327 | 
328 | </section>
329 | 
330 | <footer>
331 |     <p>This website is developed <a href="https://github.com/davidrosenberg/ml2019/">on GitHub</a>; feel free to <a href="https://github.com/davidrosenberg/ml2019/issues">report issues or send feature requests</a>.</p>
332 | </footer>
333 | 
334 | <script async defer src="scripts/navigation.js"></script>
335 | 
336 | <script>
337 |     (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
338 |         (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
339 |                              m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
340 |     })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
341 | 
342 |     ga('create', 'UA-64247420-3', 'auto');
343 |     ga('send', 'pageview');
344 | 
345 | </script>
346 | 


--------------------------------------------------------------------------------
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442 |           "dev": true,
443 |           "requires": {
444 |             "brace-expansion": "^1.1.7"
445 |           }
446 |         },
447 |         "minimist": {
448 |           "version": "0.0.8",
449 |           "resolved": "https://registry.npmjs.org/minimist/-/minimist-0.0.8.tgz",
450 |           "integrity": "sha1-hX/Kv8M5fSYluCKCYuhqp6ARsF0=",
451 |           "dev": true
452 |         },
453 |         "mkdirp": {
454 |           "version": "0.5.1",
455 |           "resolved": "https://registry.npmjs.org/mkdirp/-/mkdirp-0.5.1.tgz",
456 |           "integrity": "sha1-MAV0OOrGz3+MR2fzhkjWaX11yQM=",
457 |           "dev": true,
458 |           "requires": {
459 |             "minimist": "0.0.8"
460 |           }
461 |         },
462 |         "once": {
463 |           "version": "1.4.0",
464 |           "resolved": "https://registry.npmjs.org/once/-/once-1.4.0.tgz",
465 |           "integrity": "sha1-WDsap3WWHUsROsF9nFC6753Xa9E=",
466 |           "dev": true,
467 |           "requires": {
468 |             "wrappy": "1"
469 |           }
470 |         },
471 |         "path-is-absolute": {
472 |           "version": "1.0.1",
473 |           "resolved": "https://registry.npmjs.org/path-is-absolute/-/path-is-absolute-1.0.1.tgz",
474 |           "integrity": "sha1-F0uSaHNVNP+8es5r9TpanhtcX18=",
475 |           "dev": true
476 |         },
477 |         "sax": {
478 |           "version": "0.5.8",
479 |           "resolved": "https://registry.npmjs.org/sax/-/sax-0.5.8.tgz",
480 |           "integrity": "sha1-1HLbIo6zMcJQaw6MFVJK25OdEsE=",
481 |           "dev": true
482 |         },
483 |         "source-map": {
484 |           "version": "0.1.43",
485 |           "resolved": "https://registry.npmjs.org/source-map/-/source-map-0.1.43.tgz",
486 |           "integrity": "sha1-wkvBRspRfBRx9drL4lcbK3+eM0Y=",
487 |           "dev": true,
488 |           "requires": {
489 |             "amdefine": ">=0.0.4"
490 |           }
491 |         },
492 |         "wrappy": {
493 |           "version": "1.0.2",
494 |           "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
495 |           "integrity": "sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=",
496 |           "dev": true
497 |         }
498 |       }
499 |     },
500 |     "wrappy": {
501 |       "version": "1.0.2",
502 |       "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
503 |       "integrity": "sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=",
504 |       "dev": true
505 |     }
506 |   }
507 | }
508 | 


--------------------------------------------------------------------------------
/package.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "name": "dsga1003",
 3 |   "private": true,
 4 |   "scripts": {
 5 |     "build-in-place": "npm run template && npm run stylus",
 6 |     "template": "node ./build/templater.js index.hbs index.html",
 7 |     "stylus": "stylus styles"
 8 |   },
 9 |   "devDependencies": {
10 |     "handlebars": "^4.0.6",
11 |     "js-yaml": "^3.2.5",
12 |     "glob": "^7.1.2",
13 |     "moment": "^2.9.0",
14 |     "slugg": "^1.0.0",
15 |     "stylus": "0.54.5"
16 |   }
17 | }
18 | 


--------------------------------------------------------------------------------
/scripts/navigation.js:
--------------------------------------------------------------------------------
 1 | (function () {
 2 |     'use strict';
 3 |     var nav = document.querySelector('body > nav');
 4 |     var sections = Array.prototype.slice.call(document.querySelectorAll('body > section'));
 5 | 
 6 |     window.addEventListener('hashchange', setSelectedLink);
 7 |     window.addEventListener('scroll', setHashFromScroll);
 8 | 
 9 |     if (!window.location.hash) {
10 |         setHashFromScroll();
11 |     }
12 |     setSelectedLink();
13 | 
14 |     function setSelectedLink() {
15 |         var selected = nav.querySelector('a.selected');
16 |         if (selected) {
17 |             selected.classList.remove('selected');
18 |         }
19 | 
20 |         nav.querySelector('a[href="' + window.location.hash + '"]').classList.add('selected');
21 |     }
22 | 
23 |     function setHashFromScroll() {
24 |         var bodyScrollTop = document.body.scrollTop || document.documentElement.scrollTop;
25 |         var minDifference = +Infinity;
26 |         var minSectionId;
27 | 
28 |         sections.forEach(function (section) {
29 |             var difference = Math.abs(section.offsetTop - bodyScrollTop);
30 |             if (difference < minDifference) {
31 |                 minDifference = difference;
32 |                 minSectionId = section.id;
33 |             }
34 |         });
35 | 
36 |         // Don't want to scroll to the section directly since we are already inside a user scroll handler.
37 |         history.replaceState({}, '', '#' + minSectionId);
38 |         setSelectedLink();
39 |     }
40 | }());
41 | 


--------------------------------------------------------------------------------
/styles/colors.styl:
--------------------------------------------------------------------------------
1 | accent = rgb(87, 7, 142)
2 | accent-hover = lighten(accent, 30%)
3 | gray = #D9D9D9
4 | body-color = #444444
5 | bg-gray = #f8f7f8
6 | 


--------------------------------------------------------------------------------
/styles/phone.styl:
--------------------------------------------------------------------------------
  1 | @import "colors"
  2 | 
  3 | nav-height = 60px
  4 | number-of-nav-items = 7
  5 | 
  6 | .phone-only-block
  7 |   display: block
  8 | 
  9 | body
 10 |   padding = 10px
 11 |   padding: padding
 12 |   padding-bottom: padding + nav-height
 13 | 
 14 | body > nav
 15 |   width: 100%
 16 |   height: nav-height
 17 |   top: initial
 18 |   bottom: 0
 19 |   display: flex
 20 | 
 21 |   > a
 22 |     width: auto
 23 |     flex: 0 0 (100% / number-of-nav-items)
 24 |     border-bottom: none
 25 |     border-right: 1px solid accent-hover
 26 |     touch-action: manipulation
 27 | 
 28 |     &:hover
 29 |       width: auto
 30 | 
 31 | #this-week
 32 |   display: inline-block
 33 |   width: initial
 34 | 
 35 |   > h1
 36 |     text-align: center
 37 | 
 38 |   .week-summary
 39 |     display: inline-block
 40 | 
 41 |   section
 42 |     display: block
 43 |     padding-right: 30px
 44 |     margin-bottom: 20px
 45 |     min-width: initial
 46 |     max-width: initial
 47 | 
 48 |     &:last-child
 49 |       margin-bottom: 0
 50 | 
 51 | #home
 52 |   padding-top: 20px
 53 | 
 54 |   > h1
 55 |     text-align: center
 56 |     margin-bottom: 40px
 57 | 
 58 |     > span
 59 |       &, .department
 60 |         margin-top: 10px
 61 |         font-size: 12px
 62 |       .department
 63 |         margin-top: 5px
 64 |         display: block
 65 | 
 66 |   p
 67 |     label-width = 145px
 68 |     span
 69 |       display: inline-block
 70 |       width: "calc(100% - %s)" % label-width
 71 | 
 72 |     strong
 73 |       width: label-width
 74 | 
 75 | #textbooks
 76 |   img
 77 |     max-width: 30%
 78 |     width: auto
 79 |     height: auto
 80 |     margin-right: 5px
 81 | 
 82 | #lectures > section
 83 |   > h1
 84 |     font-size: 24px
 85 | 
 86 |   .date
 87 |     display: inline-block
 88 | 
 89 |   .topics, .references
 90 |     padding-right: 0
 91 | 
 92 |   > table
 93 |     display: block
 94 |     margin-top: 0
 95 | 
 96 |     &:not(:last-child)
 97 |       margin-bottom: 20px
 98 |       border-bottom: 2px solid gray
 99 | 
100 |     thead
101 |       display: none
102 | 
103 |     tbody
104 |       td, th
105 |         display: block
106 |         margin-bottom: 25px
107 |         padding-top: 0
108 | 
109 |     // .icon
110 |     //   &.references, &.slides
111 |     //     &::before
112 |     //       top: 0
113 |     //       left: 0
114 | 
115 |     //       margin-top: -6px
116 | 
117 |     //       font-size: 14px
118 | 
119 | #assignments .homework
120 |   strong
121 |     display: inline-block
122 | 
123 |   .deadline, .files
124 |     width: auto
125 | 
126 |   .module > *
127 |     display: block
128 |     padding-bottom: 10px
129 | 
130 |   .files > *
131 |     margin-right: 10px
132 | 
133 | #people
134 |   ul
135 |     display: block
136 | 
137 |   .person
138 |     display: block
139 |     width: auto
140 |     flex-basis: auto
141 |     margin-right: 0
142 | 
143 |     > img
144 |       float: left
145 | 


--------------------------------------------------------------------------------
/styles/style.styl:
--------------------------------------------------------------------------------
  1 | @import "colors"
  2 | 
  3 | nav-width = 60px
  4 | body-padding = 40px
  5 | body-left = nav-width + body-padding
  6 | module-padding = 20px
  7 | 
  8 | font-largest =
  9 |   font-size: 46px
 10 | 
 11 |   @media(max-width: 420px)
 12 |     font-size: 24px
 13 | 
 14 | font-large =
 15 |   font-size: 36px
 16 | 
 17 |   @media(max-width: 420px)
 18 |     font-size: 20px
 19 | 
 20 | font-medium =
 21 |   font-size: 20px
 22 | 
 23 |   @media(max-width: 420px)
 24 |     font-size: 16px
 25 | 
 26 | font-small =
 27 |   font-size: 16px
 28 | 
 29 |   @media(max-width: 420px)
 30 |     font-size: 14px
 31 | 
 32 | font-smallest =
 33 |   font-size: 14px
 34 | 
 35 | @font-face
 36 |   font-family: "Glyphicons Halflings"
 37 |   src: url("../fonts/glyphicons-halflings-regular.woff2") format("woff2"),
 38 |        url("../fonts/glyphicons-halflings-regular.woff") format("woff")
 39 | 
 40 | icon-home =
 41 |   content: "\e021"
 42 | icon-info-sign =
 43 |   content: "\e086"
 44 | icon-calendar =
 45 |   content: "\e109"
 46 | icon-list =
 47 |   content: "\e012"
 48 | icon-star =
 49 |   content: "\e006"
 50 | icon-user =
 51 |   content: "\e008"
 52 | icon-book =
 53 |   content: "\e043"
 54 | icon-pencil =
 55 |   content: "\270f"
 56 | icon-envelope =
 57 |   content: "\2709"
 58 | icon-new-window =
 59 |   content: "\e164"
 60 | icon-save =
 61 |   content: "\e166"
 62 | icon-video =
 63 |   content: "\e059"
 64 | 
 65 | 
 66 | *, *::before, *::after
 67 |   margin: 0
 68 |   padding: 0
 69 |   box-sizing: border-box
 70 | 
 71 | .phone-only-block
 72 |   display: none
 73 | 
 74 | body
 75 |   padding: body-padding
 76 |   padding-left: body-left
 77 |   padding-top: 0
 78 |   line-height: 1.6em
 79 | 
 80 |   color: body-color
 81 |   font-family: Helvetica, Arial, sans-serif
 82 | 
 83 |   background-color: bg-gray
 84 | 
 85 | a
 86 |   text-decoration: none
 87 |   color: accent
 88 | 
 89 |   &:hover
 90 |     color: accent-hover
 91 | 
 92 | h1
 93 |   text-transform: uppercase
 94 |   line-height: normal
 95 | 
 96 | p
 97 |   margin: 16px 0
 98 | 
 99 |   {font-small}
100 |   line-height: 1.6em
101 | 
102 |   &:first-child
103 |     margin-top: 0
104 | 
105 |   &:last-child
106 |     margin-bottom: 0
107 | 
108 | ul, ol
109 |   margin-left: 20px
110 | 
111 | .module
112 |   background: white
113 |   border-radius: 8px
114 |   padding: module-padding
115 |   box-shadow: 0 0 5px rgba(0,0,0,0.12)
116 | 
117 | .icon
118 |   &::before
119 |     font-family: "Glyphicons Halflings"
120 |     opacity: 0.4
121 |     margin-right: 10px
122 |     vertical-align: middle
123 |     color: accent
124 | 
125 |   &.pdf::before
126 |     {icon-new-window}
127 | 
128 |   &.zip::before
129 |     {icon-save}
130 | 
131 | body > nav
132 |   position: fixed
133 |   top: 0
134 |   left: 0
135 |   z-index: 2
136 | 
137 |   width: nav-width
138 |   height: 100%
139 | 
140 |   background-color: accent
141 |   box-shadow: 0 0 10px rgba(0,0,0,0.3)
142 | 
143 |   > a
144 |     position: relative
145 | 
146 |     display: block
147 |     width: 60px
148 |     height: nav-width
149 |     padding: 10px 10px 10px 10px
150 |     box-sizing: border-box
151 |     border-bottom: 1px solid accent-hover
152 | 
153 |     {font-smallest}
154 |     font-weight: bold
155 |     letter-spacing: .03em
156 |     text-transform: uppercase
157 |     text-align: left
158 |     color: white
159 |     white-space: nowrap
160 |     line-height: 42px
161 | 
162 |     background-color: accent
163 |     overflow: hidden
164 |     transition: width ease .2s, color ease .2s, background-color ease .2s
165 | 
166 |     &:hover
167 |       width: 200px
168 |       box-shadow: 0 0 5px rgba(0,0,0,0.3)
169 | 
170 |       color: white
171 | 
172 |     &.selected
173 |       border-bottom: none
174 | 
175 |       color: accent
176 | 
177 |       background: white
178 | 
179 |     &::before
180 |       float: left
181 | 
182 |       display: inline-block
183 |       width: nav-width
184 |       height: nav-width
185 |       margin: -10px 15px 0 -10px
186 | 
187 |       {font-medium}
188 |       font-family: "Glyphicons Halflings"
189 |       font-style: normal
190 |       font-weight: normal
191 |       letter-spacing: normal
192 |       line-height: 60px
193 |       text-align: center
194 |       vertical-align: middle
195 |       -webkit-font-smoothing: antialiased
196 |       -moz-osx-font-smoothing: grayscale
197 | 
198 |     &[href="#home"]::before
199 |       {icon-home}
200 |     &[href="#about"]::before
201 |       {icon-info-sign}
202 |     &[href="#resources"]::before
203 |       {icon-book}
204 |     &[href="#lectures"]::before
205 |       {icon-calendar}
206 |     &[href="#assignments"]::before
207 |       {icon-list}
208 |     &[href="#project"]::before
209 |       {icon-star}
210 |     &[href="#people"]::before
211 |       {icon-user}
212 | 
213 | body > section
214 |   border-bottom: 1px solid gray
215 |   padding-bottom: 70px
216 |   padding-top: 20px
217 | 
218 |   > h1
219 |     margin-bottom: 40px
220 | 
221 |     {font-large}
222 | 
223 |   > section:not(:last-of-type)
224 |     margin-bottom: 30px
225 | 
226 |   > section > h1
227 |     margin-bottom: 10px
228 | 
229 |     {font-medium}
230 | 
231 | #home
232 |   text-align: center
233 |   padding-top: body-padding
234 | 
235 |   > h1
236 |     {font-largest}
237 |     text-align: left
238 | 
239 |     margin-bottom: 50px
240 | 
241 |     > span
242 |       display: block
243 |       margin-top: 5px
244 | 
245 |       {font-medium}
246 | 
247 |     .department
248 |       {font-small}
249 |       vertical-align: middle
250 | 
251 | #course-info
252 |   margin: 10px auto
253 |   text-align: left
254 | 
255 |   th
256 |     font-weight: bold
257 |     min-width: 200px
258 |     vertical-align: top
259 | 
260 |     {font-medium}
261 |     color: accent
262 |     text-transform: uppercase
263 | 
264 |     opacity: 0.6
265 | 
266 |   tr:last-of-type
267 |     line-height: 1
268 | 
269 |   .email::before
270 |     {icon-envelope}
271 |     margin-left: 5px
272 | 
273 | #this-week
274 |   margin: 30px auto 0 auto
275 | 
276 |   width: 1220px
277 |   @media screen and (max-width: 1340px)
278 |     width: 670px
279 | 
280 |   text-align: left
281 | 
282 |   li, p
283 |     line-height: 1.1
284 | 
285 |   li
286 |     margin-bottom: 10px
287 | 
288 |   > h1
289 |     {font-large}
290 |     opacity: 0.6
291 | 
292 |   .week-summary
293 |     display: flex
294 |     flex-wrap: wrap
295 |     justify-content: center
296 | 
297 |     padding: 25px
298 |     border: 2px solid accent
299 |     border-radius: 8px
300 | 
301 |     {font-small}
302 | 
303 |     background-color: white
304 |     box-shadow: 0 0 5px rgba(0,0,0,0.12)
305 | 
306 |   h1
307 |     font-weight: bold
308 |     text-transform: uppercase
309 |     padding-bottom: 10px
310 | 
311 |   ul
312 |     list-style-type: none
313 |     margin: 0
314 | 
315 |   section
316 |     width: 250px
317 |     margin: 5px 20px
318 | 
319 |     vertical-align: top
320 | 
321 |     &.assignment
322 |       > p
323 |         margin-top: 0
324 | 
325 |       .files
326 |         display: flex
327 |         justify-content: space-between
328 |         flex-wrap: wrap
329 | 
330 | #about
331 |   .module
332 |     margin-bottom: 30px
333 | 
334 | #textbooks
335 |   img
336 |     height: 200px
337 |     margin: 0 10px 10px 0
338 | 
339 |   dd
340 |     margin-bottom: 10px
341 | 
342 |   cite
343 |     font-weight: bold
344 | 
345 | #references
346 |   ul
347 |     margin-top: 10px
348 | 
349 | #lectures
350 |   > .abbreviations
351 |     margin-bottom: 20px
352 |     list-style-type: none
353 | 
354 | #lectures > section
355 |   margin-bottom: 20px
356 |   {font-smallest}
357 | 
358 |   > table
359 |     width: 100%
360 |     margin-top: -50px
361 |     border-collapse: collapse
362 |   thead
363 |     line-height: 50px
364 | 
365 |   tbody > tr:not(:last-of-type)
366 |     border-bottom: 1px solid rgba(accent, 0.2)
367 | 
368 |   tbody > tr:not(:first-of-type)
369 |     td, th
370 |       padding-top: 15px
371 | 
372 |   th
373 |     text-align: left
374 |     text-transform: uppercase
375 |   th, td
376 |     vertical-align: top
377 | 
378 |   .label
379 |     width: 20%
380 |   .references
381 |     width: 25%
382 |   .slides
383 |     width: 20%
384 | 
385 |   ul
386 |     list-style-type: none
387 |     margin: 0
388 | 
389 |   li
390 |     line-height: normal
391 |     margin-bottom: 10px
392 | 
393 |   h1
394 |     margin-bottom: 15px
395 | 
396 |   .date, .video
397 |     display: block
398 |     margin-top: 5px
399 | 
400 |   .date
401 |     color: accent
402 |     font-size: 0.9em
403 |     opacity: 0.6
404 | 
405 |   .video
406 |     font-size: 0.85em
407 | 
408 |     &::after
409 |       font-family: "Glyphicons Halflings"
410 |       margin-left: 5px
411 |       vertical-align: top
412 |       {icon-video}
413 | 
414 |   .references
415 |     padding-right: 60px
416 | 
417 |   // .icon
418 |   //   &.references, &.slides
419 |   //     &::before
420 |   //       position: absolute
421 |   //       left: -42px
422 |   //       top: 20px
423 | 
424 |   //       font-size: 30px
425 | 
426 |   //   &.references::before
427 |   //     {icon-book}
428 | 
429 |   //   &.slides::before
430 |   //     {icon-pencil}
431 | 
432 |   //   &.video::before
433 |   //     {icon-video}
434 | 
435 | #assignments
436 |   .policies
437 |     margin-bottom: 30px
438 | 
439 |   .homework
440 |     margin-bottom: 20px
441 | 
442 |     strong
443 |       text-transform: uppercase
444 |       display: block
445 | 
446 |     .module
447 |       display: inline-block
448 |       vertical-align: middle
449 |       max-width: 600px
450 |       width: 100%
451 | 
452 |       > *
453 |         display: table-cell
454 |         padding-right: 40px
455 | 
456 |         &:last-of-type
457 |           padding-right: 0
458 | 
459 |     .title
460 |       width: 310px
461 | 
462 |       > h1
463 |         line-height: 1.6em
464 |         white-space: nowrap
465 | 
466 |       > p
467 |         margin-top: 0
468 | 
469 |     .deadline
470 |       width: 190px
471 | 
472 |     .files
473 |       width: 100px
474 | 
475 |   .submit, .solutions
476 |     display: inline-block
477 |     vertical-align: middle
478 |     margin-top: 5px
479 |     margin-left: 30px
480 | 
481 |   .submit
482 |     text-align: center
483 | 
484 |     .icon
485 |       margin-bottom: 5px
486 | 
487 |       &::before
488 |         {icon-envelope}
489 |         font-size: 32px
490 |         margin-right: 0
491 | 
492 |     a:hover
493 |       color: accent
494 | 
495 |       .icon::before
496 |         opacity: 1
497 | 
498 | #people
499 |   > section:not(:last-of-type)
500 |     margin-bottom: 50px
501 | 
502 |   > section.multiple-people
503 |     margin-bottom: 30px
504 |     max-width: 1300px
505 | 
506 |   ul
507 |     display: flex
508 |     flex-flow: row wrap
509 |     list-style-type: none
510 |     margin: 0
511 | 
512 |   .person
513 |     display: flex
514 | 
515 |     margin-right: 20px
516 |     margin-bottom: 20px
517 | 
518 |     flex-grow: 0
519 |     flex-shrink: 0
520 |     flex-basis: 600px
521 |     width: 600px
522 | 
523 |     img-size = 150px
524 | 
525 |     min-height: img-size + 2 * module-padding
526 | 
527 |     > img
528 |       width: img-size
529 |       height: img-size
530 |       margin-right: 20px
531 |       border-radius: 8px
532 | 
533 |       flex: 0 0 auto
534 | 
535 |     > .info
536 |       min-width: 0 // bizarre flexbox behavior; https://bugs.webkit.org/show_bug.cgi?id=94584#c2
537 |       > p
538 |         margin: 0 5px
539 | 
540 |         &.name
541 |           font-weight: bold
542 | 
543 |         &.email
544 |           overflow: hidden
545 |           text-overflow: ellipsis
546 | 
547 |         &.bio
548 |           margin-top: 10px
549 | 
550 |           {font-smallest}
551 | 


--------------------------------------------------------------------------------
/styles/tablet-and-phone.styl:
--------------------------------------------------------------------------------
 1 | body > section
 2 |   padding-bottom: 50px
 3 | 
 4 |   > h1
 5 |     margin-bottom: 30px
 6 | 
 7 | body > nav > a:hover
 8 |   width: auto
 9 | 
10 | #lectures > section
11 |   .label, .references, .slides
12 |     width: auto
13 | 
14 | #assignments
15 |   .solutions, .submit
16 |     margin-top: 10px
17 | 
18 |     strong
19 |       display: inline-block
20 |       margin-right: 5px
21 | 
22 |   .submit
23 |     text-align: left
24 |     .icon
25 |       display: inline-block
26 |       &::before
27 |         font-size: 16px
28 | 
29 | #this-week
30 |   width: 600px
31 | 
32 |   section
33 |     width: 220px
34 | 
35 | 


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/syllabusDS-GA1003-Spring2019.docx:
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https://raw.githubusercontent.com/davidrosenberg/ml2019/1d1caddc04ab07c796560026741d020ea0c80abc/syllabusDS-GA1003-Spring2019.docx


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/syllabusDS-GA1003-Spring2019.pdf:
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https://raw.githubusercontent.com/davidrosenberg/ml2019/1d1caddc04ab07c796560026741d020ea0c80abc/syllabusDS-GA1003-Spring2019.pdf


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/templates/_assignment-details.hbs:
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 1 | <p class="deadline"><strong>Due:</strong> {{date Due}}, {{@root.assignmentsFrontmatter.[Due time]}}</p>
 2 | <div class="files">
 3 |     {{#each PDF}}
 4 |         <a href="{{this}}" class="pdf icon">{{@key}}</a>
 5 |     {{/each}}
 6 |     {{#each ZIP}}
 7 |         <a href="{{this}}" class="zip icon">{{@key}}</a>
 8 |     {{/each}}
 9 |     {{#if noFiles}}
10 |         <p>(Coming&nbsp;soon)</p>
11 |     {{/if}}
12 | </div>
13 | 


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/templates/assignments.hbs:
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 1 | {{#each this}}
 2 |     <section class="homework" id="{{assignmentSlug this}}">
 3 |         <div class="module">
 4 |             <div class="title">
 5 |                 <h1>{{Label}}</h1>
 6 |                 <p>{{{Description}}}</p>
 7 |             </div>
 8 |             {{> _assignment-details }}
 9 |         </div>
10 |         {{#if Submittable}}
11 |             <div class="submit">
12 |                 <a href="{{@root.assignmentsFrontmatter.[Submit link]}}">
13 |                     <div class="icon"></div>
14 |                     <strong>Submit</strong>
15 |                 </a>
16 |             </div>
17 |         {{/if}}
18 |         {{#each Solutions}}
19 |             <div class="solutions">
20 |                 <strong>Solutions:</strong>
21 |                 <a href="{{Solutions}}" class="zip icon">{{@key}}</a>
22 |             </div>
23 |         {{/each}}
24 |     </section>
25 | {{/each}}
26 | 


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/templates/lectures.hbs:
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 1 | {{#each this}}
 2 |     <section class="module" id="{{lectureSlug this}}">
 3 |         <h1>{{Title}}</h1>
 4 | 
 5 |         <table>
 6 |             <thead>
 7 |                 <tr>
 8 |                     <th></th>
 9 |                     <th>Slides</th>
10 |                     <th>Notes</th>
11 |                     <th>References</th>
12 |                 </tr>
13 |             </thead>
14 |             {{#each Events}}
15 |                 <tr>
16 |                     <th class="label">
17 |                         <h1>
18 |                             {{Label}}
19 |                             <span class="date">{{shortDate @key}}</span>
20 |                             {{#if Video}}
21 |                                 <a class="video" href="{{Video}}">Video</a>
22 |                             {{/if}}
23 |                         </h1>
24 |                     </th>
25 |                     <td class="slides">
26 |                         <h1 class="phone-only-block">Slides</h1>
27 |                         {{#if Slides}}
28 |                             <ul>{{#each Slides}}
29 |                                     <li>{{maybeLink this}}</li>
30 |                                 {{/each}}
31 |                             </ul>
32 |                         {{else}}
33 |                             (None)
34 |                         {{/if}}
35 |                     </td>
36 |                     <td class="notes">
37 |                         <h1 class="phone-only-block">Notes</h1>
38 |                         {{#if Notes}}
39 |                             <ul>{{#each Notes}}
40 |                                     <li>{{maybeLink this}}</li>
41 |                                 {{/each}}
42 |                             </ul>
43 |                         {{else}}
44 |                             (None)
45 |                         {{/if}}
46 |                     </td>
47 |                     <td class="references">
48 |                         <h1 class="phone-only-block">References</h1>
49 |                         {{#if References}}
50 |                             <ul>{{#each References}}
51 |                                     <li>{{maybeLink this}}</li>
52 |                                 {{/each}}
53 |                             </ul>
54 |                         {{else}}
55 |                             (None)
56 |                         {{/if}}
57 |                     </td>
58 |                 </tr>
59 |             {{/each}}
60 |         </table>
61 |     </section>
62 | {{/each}}
63 | 


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/templates/this-week.hbs:
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 1 | {{#if lecture}}
 2 | <section id="this-week">
 3 |     <h1>This week</h1>
 4 | 
 5 |     <div class="week-summary">
 6 |         {{#with lecture}}
 7 |             <section>
 8 |                 <h1><a href="#{{lectureSlug this}}">Slides</a></h1>
 9 |                 <ul>
10 |                     {{#each Events}}
11 |                         {{#each Slides}}
12 |                             <li>{{maybeLink this}}</li>
13 |                         {{/each}}
14 |                     {{/each}}
15 |                 </ul>
16 |             </section>
17 |             <section>
18 |                 <h1><a href="#{{lectureSlug this}}">Notes</a></h1>
19 | 
20 |                 <ul>
21 |                     {{#each Events}}
22 |                         {{#each Notes}}
23 |                             <li>{{maybeLink this}}</li>
24 |                         {{/each}}
25 |                     {{/each}}
26 |                 </ul>
27 |             </section>
28 |             <section>
29 |                 <h1><a href="#{{lectureSlug this}}">References</a></h1>
30 | 
31 |                 <ul>
32 |                     {{#each Events}}
33 |                         {{#each References}}
34 |                             <li>{{maybeLink this}}</li>
35 |                         {{/each}}
36 |                     {{/each}}
37 |                 </ul>
38 |             </section>
39 |         {{/with}}
40 |         {{#with assignment}}
41 |             <section class="assignment">
42 |                 <h1><a href="#{{assignmentSlug this}}">{{Label}}</a></h1>
43 |                 <p>{{{Description}}}</p>
44 | 
45 |                 {{> _assignment-details }}
46 |             </section>
47 |         {{/with}}
48 | 
49 |         {{#if Announcement}}
50 |         <p>{{{Announcement}}}</p>
51 |         {{/if}}
52 |     </div>
53 | </section>
54 | {{/if}}
55 | 


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