├── .editorconfig ├── .gitignore ├── .travis.yml ├── README.md ├── build.sh ├── build └── templater.js ├── data ├── assignments.yml ├── lectures.yml └── this-week.yml ├── deploy.sh ├── docs ├── 10.Lab.MidtermRecap.pdf ├── 11.bayesian-networks.pdf ├── 12.Lab.bayesian-methods.pdf ├── 12.bayesian-methods.pdf ├── 13.Lab.PCA-SVD-LDA.pdf ├── 13.mixture-models.pdf ├── 14a.information-theory.pdf ├── 14b.EM-algorithm.pdf ├── 1a.course-logistics.pdf ├── 1b.intro-slt-riskdecomp.pdf ├── 1c.stoch-grad-descent.pdf ├── 2.Lab.subgradient-descent.pdf ├── 2a.excess-risk-example.pdf ├── 2b.L1L2-regularization.pdf ├── 3a.loss-functions.pdf ├── 3b.convex-optimization.pdf ├── 3c.SVM.pdf ├── 4.Lab.kernelizations.pdf ├── 4a.kernels-highlevel.pdf ├── 4b.features.pdf ├── 4c.kernels.pdf ├── 5.Lab.misc.pdf ├── 5a.trees.pdf ├── 5b.bias-variance.pdf ├── 6a.bagging-random-forests.pdf ├── 6b.boosting.pdf ├── 7a.gradient-boosting.pdf ├── 7b.neural-networks.pdf ├── 8.Lab.glm.pdf ├── 8a.citysense.pdf ├── 8b.conditional-probability-models.pdf ├── 9.midterm-review.pdf ├── convex-optimization.pdf ├── matrix-differentiation.pdf ├── probability-distributions.pdf ├── projections-svm.pdf ├── svm-notes.pdf └── syllabusDS-GA1003-Spring2015.pdf ├── favicon.ico ├── fonts ├── glyphicons-halflings-regular.woff └── glyphicons-halflings-regular.woff2 ├── gulpfile.js ├── homework ├── hw1.pdf ├── hw1.zip ├── hw2.pdf ├── hw2.zip ├── hw3.pdf ├── hw3.zip ├── hw4.pdf ├── hw4.zip ├── hw5.pdf ├── hw5.zip ├── hw6.pdf ├── hw6.zip ├── hw7.pdf └── hw7.zip ├── images ├── boyd-1x.jpg ├── boyd-2x.jpg ├── boyd-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 │ ├── brian.jpg │ ├── david.jpg │ ├── gideon.jpg │ ├── haoxu.jpg │ ├── kurt.jpg │ ├── kush.jpg │ ├── prasoon.jpg │ ├── ranbi.jpg │ ├── shanshan.jpg │ └── vikas.jpg ├── index.hbs ├── package.json ├── refs ├── PegasosMPB.pdf ├── barnes-MatrixDifferentiation.pdf ├── bottou-sgd-tricks-2012.pdf ├── boyd-slides │ ├── functions.pdf │ └── sets.pdf ├── bv_cvxbook.pdf ├── bv_subgradients_notes.pdf ├── coordinate-descent.pdf ├── felippa-MatrixCalculus.pdf ├── lasso-original.pdf ├── matrixCookbook-15Nov2012.pdf ├── pml-intro-22may12.pdf ├── shalev-shwartz11a-SCD-L1.pdf └── stanfordCS229-linalg.pdf ├── scripts └── navigation.js ├── styles ├── colors.styl ├── phone.styl ├── style.styl └── tablet-and-phone.styl └── templates ├── _assignment-details.hbs ├── _references.hbs ├── assignments.hbs ├── lectures.hbs └── this-week.hbs /.editorconfig: -------------------------------------------------------------------------------- 1 | root = true 2 | 3 | [*] 4 | end_of_line = lf 5 | insert_final_newline = true 6 | charset = utf-8 7 | indent_style = space 8 | trim_trailing_whitespace = true 9 | 10 | [*.{js,html,hbs}] 11 | indent_size = 4 12 | 13 | [{*.styl,*.yml}] 14 | indent_size = 2 15 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .dropbox 2 | .Rhistory 3 | desktop.ini 4 | Thumbs.db 5 | npm-debug.log 6 | *.*~ 7 | ~$* 8 | \#*\# 9 | 10 | *.css 11 | index.html 12 | out/ 13 | node_modules/ 14 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | language: node_js 2 | node_js: 3 | - stable 4 | script: bash ./deploy.sh 5 | env: 6 | global: 7 | - GH_REF: github.com/davidrosenberg/dsga1003.git 8 | - secure: FzCgD214+KRyxvVTut84DrKQJ36BmZTy0yUNgywFecYEEnfmTl3dHquk2GwvP191mIoBqpCpsZiLMJ9vHSKF47NJkORwwIwesnuBjEiJxzG/uxpyNgt1Dxn4JJFW8ymPjxrI2RlNnVnhGVHyndAFHfHH9FJqGf6AlujGQdf3zMU= 9 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DS-GA 1003 Website 2 | 3 | ## Development info 4 | 5 | ### Building locally: quickstart 6 | 7 | Be sure to have [Node.js](https://iojs.org/) 5.x+ installed. 8 | 9 | Run `npm install` in the project root. This will install some build tools we use. 10 | 11 | Run `npm run watch` to start a watcher that will compile things in place. You can then view the site by simply opening `index.html`. 12 | 13 | Run `npm run build` 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 the build process is slightly more complicated than the in-place watch process. 14 | 15 | ### Technologies used 16 | 17 | [Stylus](https://learnboost.github.io/stylus/) is used for styling. 18 | 19 | [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`. 20 | 21 | [Gulp](http://gulpjs.com/) is used for a build/watch system. This is abstracted behind npm tasks though (`npm run watch`, `npm run build`). 22 | 23 | [Traceur](https://github.com/google/traceur-compiler) is used to write ES2015 code instead of boring-old-JavaScript, for the build code at least. 24 | 25 | [Travis](https://travis-ci.org/) is used to build and deploy the website to gh-pages on each push, per [this guide](https://gist.github.com/domenic/ec8b0fc8ab45f39403dd). 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 `
` as if it were `
`). To preview the document outline, use the [HTML 5 Outliner tool](https://gsnedders.html5.org/outliner/); here's [a direct link to the outline for the current version](https://gsnedders.html5.org/outliner/process.py?url=https%3A%2F%2Fdavidrosenberg.github.io%2Fml2015%2F%23home). 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 -rf out || exit 0; 8 | mkdir out; 9 | 10 | rm -f index.html 11 | rm -rf styles/*.css 12 | 13 | npm run build-in-place 14 | 15 | cp index.html favicon.ico out/ 16 | 17 | mkdir out/styles/ 18 | cp styles/*.css out/styles/ 19 | 20 | cp -r refs images homework fonts scripts docs out/ 21 | 22 | echo "" 23 | find out/ -print 24 | echo "" 25 | -------------------------------------------------------------------------------- /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('to-slug'); 9 | 10 | const SLIDES_AND_NOTES = 'Slides and Notes'; 11 | const REFERENCES_PRE = 'References (Pre)'; 12 | const REFERENCES_SUPPLEMENTAL = 'References (Supplemental)'; 13 | 14 | const TEMPLATES_DIR = path.resolve(__dirname, '../templates'); 15 | 16 | module.exports = (input, output) => { 17 | const handlebars = handlebarsFactory.create(); 18 | registerHelpers(handlebars); 19 | registerPartials(handlebars); 20 | 21 | const template = compileTemplate(handlebars, input); 22 | const documents = parseDocuments(); 23 | fs.writeFileSync(output, template(documents)); 24 | }; 25 | 26 | // Uncomment to see documents; useful while tweaking the templates 27 | // console.log(require("util").inspect(documents, { depth: Infinity })); 28 | 29 | function compileTemplate(handlebars, input) { 30 | return handlebars.compile(fs.readFileSync(input, { encoding: 'utf-8' })); 31 | } 32 | 33 | function parseDocuments() { 34 | const lectures = yaml.safeLoad(fs.readFileSync(path.resolve(__dirname, '../data/lectures.yml'))); 35 | 36 | // Pull Slides and Notes, References (Pre), and References (Supplemental) from the topics up to the top level: 37 | for (const lecture of lectures) { 38 | Object.keys(lecture.Events).forEach(eventDate => { 39 | const event = lecture.Events[eventDate]; 40 | 41 | ensureArrayExists(event, SLIDES_AND_NOTES); 42 | ensureArrayExists(event, REFERENCES_PRE); 43 | ensureArrayExists(event, REFERENCES_SUPPLEMENTAL); 44 | 45 | Object.keys(event.Topics).forEach(topic => { 46 | copyArrayInto(event.Topics[topic], event, SLIDES_AND_NOTES); 47 | copyArrayInto(event.Topics[topic], event, REFERENCES_PRE); 48 | copyArrayInto(event.Topics[topic], event, REFERENCES_SUPPLEMENTAL); 49 | }); 50 | }); 51 | } 52 | 53 | let assignmentsFrontmatter, assignments; 54 | let i = 0; 55 | yaml.safeLoadAll(fs.readFileSync(path.resolve(__dirname, '../data/assignments.yml')), doc => { 56 | switch (i) { 57 | case 0: 58 | assignmentsFrontmatter = doc; 59 | break; 60 | case 1: 61 | assignments = doc; 62 | break; 63 | default: 64 | throw new Error('Cannot have more than two documents in assignments.yaml'); 65 | } 66 | ++i; 67 | }); 68 | 69 | const thisWeek = yaml.safeLoad(fs.readFileSync(path.resolve(__dirname, '../data/this-week.yml'))); 70 | 71 | // Pull data from lectures and assignments into thisWeek: 72 | const thisWeekLecture = lectures.find(l => l.Title === thisWeek['Lecture/Lab']); 73 | const thisWeekAssignment = assignments.find(a => a.Label === thisWeek.Assignment); 74 | 75 | if (thisWeekLecture === undefined) { 76 | throw new Error(`Could not find entry in lectures.yml with Title "${thisWeek['Lecture/Lab']}" specified in ` + 77 | `this-week.yaml`); 78 | } 79 | if (thisWeekAssignment === undefined) { 80 | throw new Error(`Could not find entry in assignments.yml with Label "${thisWeek['Assignment']}" specified in ` + 81 | `this-week.yaml`); 82 | } 83 | 84 | thisWeek.lecture = thisWeekLecture; 85 | thisWeek.assignment = thisWeekAssignment; 86 | 87 | return { lectures, thisWeek, assignmentsFrontmatter, assignments }; 88 | } 89 | 90 | function registerPartials(handlebars) { 91 | for (const filename of fs.readdirSync(TEMPLATES_DIR)) { 92 | const filePath = path.resolve(TEMPLATES_DIR, filename); 93 | const partialName = path.basename(filename, '.hbs'); 94 | const contents = fs.readFileSync(filePath, { encoding: 'utf-8' }); 95 | 96 | handlebars.registerPartial(partialName, contents); 97 | } 98 | } 99 | 100 | function registerHelpers(handlebars) { 101 | handlebars.registerHelper('date', d => moment.utc(new Date(d).toISOString()).format('MMMM Do')); 102 | handlebars.registerHelper('shortDate', d => moment.utc(new Date(d).toISOString()).format('MMM D')); 103 | handlebars.registerHelper('maybeLink', v => { 104 | if (typeof v === 'string') { 105 | return v; 106 | } 107 | 108 | assert (typeof v === 'object' && v !== null, 'Links must be either strings or objects'); 109 | 110 | const keys = Object.keys(v); 111 | assert(keys.length === 1, 'Link objects must have a single key'); 112 | const key = keys[0]; 113 | 114 | return new handlebars.SafeString('' + key + ''); 115 | }); 116 | handlebars.registerHelper('lectureSlug', l => 'lecture-' + toSlug(l.Title)); 117 | handlebars.registerHelper('assignmentSlug', l => 'assignment-' + toSlug(l.Label)); 118 | } 119 | 120 | function ensureArrayExists(obj, prop) { 121 | if (!(prop in obj)) { 122 | obj[prop] = []; 123 | } 124 | } 125 | 126 | function copyArrayInto(source, dest, keyName) { 127 | if (source && source[keyName]) { 128 | dest[keyName].push(...source[keyName]); 129 | } 130 | } 131 | -------------------------------------------------------------------------------- /data/assignments.yml: -------------------------------------------------------------------------------- 1 | Due time: 4pm 2 | Submit link: "https://newclasses.nyu.edu/portal" 3 | --- 4 | - 5 | Label: Homework 1 6 | Description: Ridge regression and SGD 7 | Due: 2015-02-06 8 | PDF: {hw1.pdf: "homework/hw1.pdf"} 9 | ZIP: {hw1.zip: "homework/hw1.zip"} 10 | # Solutions: {hw1_solutions.zip: "homework/hw1-sgd/hw1_solutions.zip"} 11 | - 12 | Label: Homework 2 13 | Description: Lasso regression 14 | Due: 2015-02-13 15 | PDF: {hw2.pdf: "homework/hw2.pdf"} 16 | ZIP: {hw2.zip: "homework/hw2.zip"} 17 | - 18 | Label: Homework 3 19 | Description: SVM and Sentiment Analysis 20 | Due: 2015-02-23 21 | PDF: {hw3.pdf: "homework/hw3.pdf"} 22 | ZIP: {hw3.zip: "homework/hw3.zip"} 23 | - 24 | Label: Homework 4 25 | Description: Kernel Methods and Lagrangian Duality 26 | Due: 2015-03-03 27 | PDF: {hw4.pdf: "homework/hw4.pdf"} 28 | ZIP: {hw4.zip: "homework/hw4.zip"} 29 | - 30 | Label: Homework 5 31 | Description: Trees and Ensemble Methods 32 | Due: 2015-03-25 33 | PDF: {hw5.pdf: "homework/hw5.pdf"} 34 | ZIP: {hw5.zip: "homework/hw5.zip"} 35 | - 36 | Label: Homework 6 37 | Description: Midterm Review 38 | Due: 2015-04-10 39 | PDF: {hw6.pdf: "homework/hw6.pdf"} 40 | ZIP: {hw6.zip: "homework/hw6.zip"} 41 | - 42 | Label: Homework 7 43 | Description: Tikhonov, Ivanov, Square Hinge, and GLMs 44 | Due: 2015-04-21 45 | PDF: {hw7.pdf: "homework/hw7.pdf"} 46 | ZIP: {hw7.zip: "homework/hw7.zip"} 47 | -------------------------------------------------------------------------------- /data/lectures.yml: -------------------------------------------------------------------------------- 1 | #http://techtalks.tv/machine-learning-nyu-spring-2015/ 2 | - 3 | Title: Week 1 4 | Events: 5 | 2015-01-28: 6 | Label: Lecture 7 | Video: "http://techtalks.tv/talks/lecture-1-9/61396/" 8 | Topics: 9 | Course mechanics: 10 | References (Supplemental): 11 | - {KPM Ch 1: "http://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf"} 12 | Slides and Notes: 13 | - {Course Logistics: "docs/1a.course-logistics.pdf"} 14 | Statistical learning theory framework: 15 | References (Pre): 16 | - Math review notes 17 | Slides and Notes: 18 | - {Statistical Learning Theory: "docs/1b.intro-slt-riskdecomp.pdf"} 19 | Excess risk decomposition: 20 | Gradient and stochastic gradient descent: 21 | References (Supplemental): 22 | - {Bottou's SGD Tricks: "refs/bottou-sgd-tricks-2012.pdf"} 23 | - BV Preface, Ch 1 24 | Slides and Notes: 25 | - {Stochastic Gradient Descent: "docs/1c.stoch-grad-descent.pdf"} 26 | 2015-01-29: 27 | Label: Lab 28 | Video: "http://techtalks.tv/talks/lab-1/61397/" 29 | Topics: 30 | Matrix differentiation (Shanshan Ding): 31 | References (Supplemental): 32 | - {Barnes's "Matrix Differentiation" notes: "http://www.atmos.washington.edu/~dennis/MatrixCalculus.pdf"} 33 | - {Felippa's "Matrix Calculus" chapter: "http://www.colorado.edu/engineering/cas/courses.d/IFEM.d/IFEM.AppC.d/IFEM.AppC.pdf"} 34 | Slides and Notes: 35 | - {Matrix Differentiation: "docs/matrix-differentiation.pdf"} 36 | # References (Supplemental): 37 | # - {The Matrix Cookbook: "refs/matrixCookbook-15Nov2012.pdf"} 38 | - 39 | Title: Week 2 40 | Events: 41 | 2015-02-04: 42 | Label: Lecture 43 | Video: "http://techtalks.tv/talks/lecture-44/61399/" 44 | Topics: 45 | Excess Risk Decomposition (cont.): 46 | References (Pre): 47 | - "KPM 16.2.1" 48 | Slides and Notes: 49 | - {Excess Risk Demo: "docs/2a.excess-risk-example.pdf"} 50 | L1/L2 regularization: 51 | References (Supplemental): 52 | - "KPM 13.3, 13.4.1" 53 | - "HTF Ch. 3" 54 | - {Bottou's SGD Tricks: "refs/bottou-sgd-tricks-2012.pdf"} 55 | Slides and Notes: 56 | - {L1 and L2 regularization: "docs/2b.L1L2-regularization.pdf"} 57 | Optimization methods for Lasso: 58 | 2015-02-05: 59 | Label: Lab 60 | Topics: 61 | Subgradient descent: 62 | References (Supplemental): 63 | - {Boyd's subgradient notes: "http://web.stanford.edu/class/ee364b/lectures.html"} 64 | Slides and Notes: 65 | - {Subgradient Methods: "docs/2.Lab.subgradient-descent.pdf"} 66 | - 67 | Title: Week 3 68 | Events: 69 | 2015-02-11: 70 | Label: Lecture 71 | Video: "http://techtalks.tv/talks/lecture-47/61403/" 72 | Topics: 73 | Loss functions: 74 | Slides and Notes: 75 | - {Loss functions: "docs/3a.loss-functions.pdf"} 76 | Convex Optimization: 77 | Slides and Notes: 78 | - {Convex optimization: "docs/3b.convex-optimization.pdf"} 79 | - {Extreme Abridgement of BV: "docs/convex-optimization.pdf"} 80 | References (Supplemental): 81 | - BV Ch 1-5 82 | SVM: 83 | References (Supplemental): 84 | - "HTF 12.2.1 - 12.2.2" 85 | Slides and Notes: 86 | - {SVM (slides): "docs/3c.SVM.pdf"} 87 | - {SVM (notes): "docs/svm-notes.pdf"} 88 | 89 | 2015-02-12: 90 | Label: Lab 91 | Video: "http://techtalks.tv/talks/lab-11/61404/" 92 | Topics: 93 | Projections: 94 | SVM (Geometric Motivation): 95 | References (Supplemental): 96 | - "HTF 3.2.0,4.5" 97 | - "HTF pp. 417-419" 98 | Slides and Notes: 99 | - {Projections and SVM: "docs/projections-svm.pdf"} 100 | - 101 | Title: Week 4 102 | Events: 103 | 2015-02-18: 104 | Label: Lecture 105 | Video: "http://techtalks.tv/talks/lecture-50/61408/" 106 | Topics: 107 | Features: 108 | Slides and Notes: 109 | - {Features: "docs/4b.features.pdf"} 110 | Kernel Methods: 111 | References (Supplemental): 112 | - "KPM 14.2.1-14.2.6" 113 | Slides and Notes: 114 | - {Kernel Methods (Overview): "docs/4a.kernels-highlevel.pdf"} 115 | - {Kernels: "docs/4c.kernels.pdf"} 116 | Kernelized Ridge Regression: 117 | References (Supplemental): 118 | - "KPM 14.3.1-14.4.3" 119 | Kernelized SVM: 120 | References (Supplemental): 121 | - "HTF 12.3.1" 122 | - "KPM 14.5" 123 | 124 | 2015-02-19: 125 | Label: Lab 126 | Topics: 127 | Kernelizations: 128 | Slides and Notes: 129 | - {Kernelizations: "docs/4.Lab.kernelizations.pdf"} 130 | 131 | - Title: Week 5 132 | Events: 133 | 2015-02-25: 134 | Label: Lecture 135 | Video: "http://techtalks.tv/talks/lecture-53/61414/" 136 | Topics: 137 | Trees: 138 | Slides and Notes: 139 | - {Trees: "docs/5a.trees.pdf"} 140 | References (Supplemental): 141 | - "HTF 9.2" 142 | - "KPM 16-16.2.4" 143 | Bias and Variance: 144 | Slides and Notes: 145 | - {Bias and Variance: "docs/5b.bias-variance.pdf"} 146 | 2015-02-26: 147 | Label: Lab 148 | Video: "http://techtalks.tv/talks/lab-16/61415/" 149 | Topics: 150 | Homework Review: 151 | Slides and Notes: 152 | - {"SGD, Parameter Tuning, and Directional Derivatives": "docs/5.Lab.misc.pdf"} 153 | 154 | - Title: Week 6 155 | Events: 156 | 2015-03-04: 157 | Label: Lecture 158 | Video: "http://techtalks.tv/talks/lecture-55/61417/" 159 | Topics: 160 | Bagging: 161 | Slides and Notes: 162 | - {Bagging and Random Forests: "docs/6a.bagging-random-forests.pdf"} 163 | References (Supplemental): 164 | - "HTF 8.7" 165 | Random Forests: 166 | References (Supplemental): 167 | - "HTF Ch. 15" 168 | Boosting: 169 | References (Supplemental): 170 | - "HTF Ch. 10" 171 | - "KPM Ch. 16.4" 172 | Slides and Notes: 173 | - {Boosting: "docs/6b.boosting.pdf"} 174 | 2015-03-05: 175 | Label: Lab 176 | Video: "http://techtalks.tv/talks/lab-18/61420/" 177 | Topics: 178 | Probability Distribution Review (Shanshan Ding): 179 | Slides and Notes: 180 | - {Probability Distributions: "docs/probability-distributions.pdf"} 181 | 182 | - Title: Week 7 183 | Events: 184 | 2015-03-11: 185 | Label: Lecture 186 | Video: "http://techtalks.tv/talks/lecture-58/61424/" 187 | Topics: 188 | Gradient Boosting: 189 | References (Supplemental): 190 | - {Friedman's GBM Paper: http://statweb.stanford.edu/~jhf/ftp/trebst.pdf} 191 | - {Ridgeway's GBM Guide: http://www.saedsayad.com/docs/gbm2.pdf} 192 | Slides and Notes: 193 | - {Gradient Boosting: "https://davidrosenberg.github.io/mlcourse/Archive/2015/Lectures/7a.gradient-boosting.pdf"} 194 | Neural Networks: 195 | Slides and Notes: 196 | - {Neural Networks: "docs/7b.neural-networks.pdf"} 197 | References (Supplemental): 198 | - "HTF Ch 11" 199 | - "KPM 16.5-16.5.5" 200 | - {Bengio's "Practical Recommendations": http://arxiv.org/pdf/1206.5533v2.pdf} 201 | 2015-03-12: 202 | Label: Project Adviser Meetings 203 | Topics: [] 204 | 205 | - Title: Week 8 206 | Events: 207 | 2015-03-25: 208 | Label: Lecture 209 | Video: "http://techtalks.tv/talks/lecture-61/61433/" 210 | Topics: 211 | Probabilistic Modeling: 212 | Slides and Notes: 213 | - {CitySense and CabSense: "docs/8a.citysense.pdf"} 214 | Conditional Probability Models: 215 | Slides and Notes: 216 | - {Conditional Probability Models: "docs/8b.conditional-probability-models.pdf"} 217 | 2015-03-26: 218 | Label: Lab 219 | Video: "http://techtalks.tv/talks/lab-22/61435/" 220 | Topics: 221 | Generalized Linear Models: 222 | Slides and Notes: 223 | - {Generalized Linear Models: "docs/8.Lab.glm.pdf"} 224 | References (Supplemental): 225 | - {Part III of Andrew Ng's CS 229 Notes: http://cs229.stanford.edu/notes/cs229-notes1.pdf} 226 | 227 | 228 | - Title: Week 9 229 | Events: 230 | 2015-04-01: 231 | Label: Lecture 232 | Video: "http://techtalks.tv/talks/lecture-64/61438/" 233 | Topics: 234 | Midterm Exam Review: 235 | Slides and Notes: 236 | - {Midterm Review: "docs/9.midterm-review.pdf"} 237 | 2015-04-02: 238 | Label: Lab 239 | Video: "http://techtalks.tv/talks/lecture-64/61440/" 240 | Topics: 241 | Midterm Exam Review: 242 | 243 | - Title: Week 10 244 | Events: 245 | 2015-04-08: 246 | Label: Midterm Exam 247 | Topics: [] 248 | 2015-04-09: 249 | Label: Midterm Recap 250 | Topics: 251 | Midterm Recap: 252 | Slides and Notes: 253 | - {Midterm Recap: "docs/10.Lab.MidtermRecap.pdf"} 254 | 255 | - Title: Week 11 256 | Events: 257 | 2015-04-15: 258 | Label: Lecture 259 | Video: "http://techtalks.tv/talks/lecture-69/61447/" 260 | Topics: 261 | Bayesian Networks: 262 | Slides and Notes: 263 | - {Bayesian Networks: "docs/11.bayesian-networks.pdf"} 264 | References (Supplemental): 265 | - "KPM Chapter 10" 266 | 267 | 2015-04-16: 268 | Label: Project Adviser Meetings 269 | Topics: [] 270 | 271 | - Title: Week 12 272 | Events: 273 | 2015-04-22: 274 | Label: Lecture 275 | Video: http://techtalks.tv/talks/lecture-72/61451/ 276 | Topics: 277 | Bayesian Methods: 278 | References (Supplemental): 279 | - "KPM Chapter 5" 280 | Slides and Notes: 281 | - {Bayesian Methods: "docs/12.bayesian-methods.pdf"} 282 | Bayesian Regression: 283 | References (Supplemental): 284 | - "KPM 7.6" 285 | 286 | 2015-04-23: 287 | Label: Lab 288 | Video: "http://techtalks.tv/talks/lab-29/61453/" 289 | Topics: 290 | Beta-Binomial Model: 291 | Naive Bayes: 292 | Bag-of-Words (Bernoulli Version): 293 | Slides and Notes: 294 | - {Bayesian Review and Naive Bayes: "docs/12.Lab.bayesian-methods.pdf"} 295 | 296 | - Title: Week 13 297 | Events: 298 | 2015-04-29: 299 | Label: Lecture 300 | Video: "http://techtalks.tv/talks/lecture-74/61456/" 301 | Topics: 302 | k-means Clustering: 303 | Gaussian Mixture Models: 304 | EM Algorithm (Introduction): 305 | Slides and Notes: 306 | - {k-means and Mixture Models: "docs/13.mixture-models.pdf"} 307 | 2015-04-30: 308 | Label: Lab (Shanshan) 309 | Video: "http://techtalks.tv/talks/lab-31/61457/" 310 | Topics: 311 | SVD, PCA, Linear Discriminant Analysis: 312 | Slides and Notes: 313 | # - {PCA, SVD, and Fisher's LDA: "docs/13.Lab.PCA-SVD-LDA.pdf"} 314 | - {PCA-SVD-LDA: "docs/13.Lab.PCA-SVD-LDA.pdf"} 315 | 316 | - Title: Week 14 317 | Events: 318 | 2015-05-06: 319 | Label: Lecture 320 | Video: "http://techtalks.tv/talks/lecture-77/61460/" 321 | Topics: 322 | Information Theory: 323 | Slides and Notes: 324 | - {Information Theory: "docs/14a.information-theory.pdf"} 325 | EM Algorithm (General): 326 | Slides and Notes: 327 | - {EM Algorithm: "docs/14b.EM-algorithm.pdf"} 328 | 329 | 2015-05-07: 330 | Label: Project Adviser Meetings 331 | Topics: [] 332 | -------------------------------------------------------------------------------- /data/this-week.yml: -------------------------------------------------------------------------------- 1 | Lecture/Lab: Week 14 2 | Assignment: Homework 7 3 | -------------------------------------------------------------------------------- /deploy.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -e # exit with nonzero exit code if anything fails 3 | 4 | # Put stuff in the out directory 5 | npm run build 6 | 7 | # go to the out directory and create a *new* Git repo 8 | cd out 9 | git init 10 | 11 | # inside this git repo we'll pretend to be a new user 12 | git config user.name "Travis CI" 13 | git config user.email "d@domenic.me" 14 | 15 | # The first and only commit to this new Git repo contains all the 16 | # files present with the commit message "Deploy to GitHub Pages". 17 | git add . 18 | git commit -m "Deploy to GitHub Pages" 19 | 20 | # Force push from the current repo's master branch to the remote 21 | # repo's gh-pages branch. (All previous history on the gh-pages branch 22 | # will be lost, since we are overwriting it.) 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templater = require('./build/templater.js'); 3 | const fs = require('fs'); 4 | const gulp = require('gulp'); 5 | const gutil = require('gulp-util'); 6 | const gulpStylus = require('gulp-stylus'); 7 | 8 | const STYLUS = 'styles/*.styl'; 9 | const TEMPLATES = '**/*.hbs'; 10 | const MAIN_TEMPLATE = 'index.hbs'; 11 | const DATA = 'data/*.yml'; 12 | 13 | const OUTPUT_HTML = 'index.html'; 14 | const OUTPUT_STYLES = 'styles/'; 15 | 16 | gulp.task('template', () => { 17 | try { 18 | templater(MAIN_TEMPLATE, OUTPUT_HTML) 19 | } catch (e) { 20 | // Delete the index.html file to make it clear that the templating failed 21 | deleteIfExists(OUTPUT_HTML); 22 | 23 | gutil.log(gutil.colors.red(`Deleting ${OUTPUT_HTML} since templating failed`)); 24 | throw e; 25 | } 26 | }); 27 | 28 | gulp.task('stylus', () => 29 | gulp.src(STYLUS) 30 | .pipe(gulpStylus()) 31 | .pipe(gulp.dest(OUTPUT_STYLES)) 32 | ); 33 | 34 | gulp.task('build', ['template', 'stylus']); 35 | 36 | gulp.task('watch', ['template', 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2015 5 | 6 | 7 | 8 | 9 | 10 | 11 | 20 | 21 |
22 |

23 | Machine Learning and Computational Statistics 24 | 25 | DS-GA 1003 · Spring 2015 · 26 | NYU Center for Data Science 27 | 28 |

29 | 30 | 31 | 32 | 35 | 36 | 39 | 40 | 43 | 44 | 47 | 48 | 50 |
Instructor 33 | David Rosenberg 34 |
Lecture 37 | Wednesdays 5:10pm–7pm, Warren Weaver Hall 109 38 |
Lab 41 | Thursday 6:10pm–7pm, Warren Weaver Hall 109 42 |
Office Hours 45 | Instructor: Thursday 7pm–8pm, Warren Weaver Hall 109 46 |
Graders: Monday 2pm–4pm / Tuesday 1pm–2pm, CDS common area 49 |
51 | 52 | {{> this-week thisWeek }} 53 |
54 |
55 |

About This Course

56 | 57 |
58 |

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 both the traditional and the novel data science problems found in practice. This course will also serve as a foundation on which more specialized courses and further independent study can build.

59 | 60 |

This is a required course for the Center for Data Science's Masters degree in Data Science, and the course is designed for the students in this program. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that this class is a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this class (e.g. data cleaning, cross-validation, decision trees, and sampling bias).

61 | 62 |

Course details can be found in the syllabus.

63 | 64 |

This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the Lab Instructor, graders, and the Instructor. Rather than emailing questions to the teaching staff, you are encouraged to post your questions on Piazza. If you have any problems or feedback for the developers, email team@piazza.com. Without registering, you can also view an anonymized version of our Piazza board.

65 | 66 |

See the Course Calendar for all schedule information.

67 | 68 |

For registration information, please contact Varsha Tiger. 69 |

70 | 71 |
72 |

Prerequisites

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    74 |
  • DS-GA-1001: Intro to Data Science or its equivalent 75 |
  • Solid mathematical background, equivalent to a 1-semester undergraduate course in each of the following: linear algebra, multivariate calculus (primarily differential calculus), probability theory, and statistics. 76 |
  • Python programming required for all homework assignments (not necessary for auditors) 77 |
  • Recommended: Computer science background up to a "data structures and algorithms" course 78 |
  • Recommended: At least one advanced, proof-based mathematics course 79 |
  • Some prerequisites may be waived with permission of the instructor. 80 |
81 |
82 |
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Grading

84 |

Problem sets (60%) + Midterm exam (20%) + Project (20%)

85 |

86 | Up to (2%) extra credit can be earned by answering student questions on Piazza and for positive contributions to class and lab discussions; there will be additional extra credit opportunities in the homework assignments in the form of optional problems and competitions. 87 |

88 |

The course conforms to NYU’s policy on academic integrity for students.

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91 | 92 |
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Resources

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96 |

Textbooks

97 | 98 | The cover of Machine Learning: a Probabilistic Perspective 99 | The cover of Elements of Statistical Learning 100 | The cover of Convex Optimization 101 | The cover of An Introduction to Statistical Learning 102 | 103 |
104 |
Machine Learning: A Probabilistic Perspective (Kevin P. Murphy) 105 |
This will be the required textbook for the class. It was chosen for several reasons: First, it covers an unusually broad set of topics, and it will serve you well as a reference for many topics beyond the scope of this course. Second, it is has a surprisingly extensive coverage of recent advances in the field, incorporating material that would otherwise only be available in research papers. Finally, if you're a Matlab coder, there is extensive software support. 106 | 107 |
The 108 | Elements of Statistical Learning (Hastie, Friedman, and Tibshirani) 109 |
This book is available as a free PDF, and it will 110 | serve as a nice complement to Murphy's book. Despite its popularity and the pretty pictures, this is not an easy book. It's written by three statisticians who invented many of the techniques discussed. 111 | 112 |
Convex Optimization (Boyd and Vandenberghe) 113 |
This book (also available as a free PDF) was an instant hit in the machine learning community when it was published in 2004. Most of the optimization problems that we know how to solve are convex problems. We will be making light use of this book, mostly for its coverage of Lagrangians and duality. However, it's a good book to get familiar with, as it's very well written and it covers a lot of techniques used in more advanced machine learning literature. 114 | 115 |
An Introduction to Statistical Learning (James, Witten, Hastie, and Tibshirani) 116 |
This book (also available as a free PDF), is written 117 | 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. 118 |
119 |
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122 |

Other tutorials and references

123 | 124 |

(If you find additional references that you recommend, please share them on Piazza and we can add them here.) 125 | 126 |

133 |
134 | 135 |
136 |

Software

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    139 |
  • CVXPY and CVXOPT are for solving convex optimization problems in Python. 140 |
  • Theano has symbolic differentiation and facilitates GPU usage. It's a favorite for some neural networks folks. 141 |
  • NumPy is "the fundamental package for scientific computing with Python." Our homework assignments will use NumPy arrays extensively. 142 |
143 |
144 |
145 | 146 |
147 |

Lectures

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158 |

Assignments

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161 |

Homework Submission: Homework should be submitted through NYU Classes. 162 | 163 |

Late Policy: 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. 164 | 165 |

Collaboration Policy: 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. 166 |

167 | 168 | {{> assignments assignments }} 169 |
170 | 171 | 176 | 177 |
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People

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Instructor

182 | 183 |
184 | A photo of David Rosenberg 185 |
186 |

David Rosenberg 187 |

David is Chief Scientist of YP Mobile Labs at YP. 189 |

190 |
191 |
192 | 193 |
194 |

Lab Instructor (Mathematical Foundations)

195 | 196 |
197 | A photo of Shanshan Ding 198 |
199 |

Shanshan 200 | Ding 201 |

Shanshan is a research data scientist 202 | in 203 | the YP 204 | Mobile Labs group at YP. 205 |

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207 |
208 | 209 | 210 |
211 |

Graders

212 | 213 |
    214 |
  • 215 | A photo of Hao Xu 216 |
    217 |

    Hao Xu 218 |

    Hao is a second year student in CDS Data Science Masters program. In the fall he will begin as a data scientist at AIG. 220 |

    221 |
  • 222 | A photo of Ran Bi 223 |
    224 |

    Ran Bi 225 |

    Ran is currently a second year student in the Data Science program at NYU. She will begin working on the AIG science team in Summer 2015. 227 |

    228 |
  • 229 | A photo of Prasoon Goyal 230 |
    231 |

    Prasoon Goyal 232 |

    Prasoon is currently a Masters student in Computer Science at NYU. 234 |

    235 |
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Project Advisers

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    242 |
  • 243 | A photo of Gideon Mann 244 |
    245 |

    Gideon Mann 246 |

    Gideon is currently the Head of Data Science in the CTO office at Bloomberg LP. 247 |

    248 |
  • 249 | A photo of Kurt Miller 250 |
    251 |

    Kurt Miller 252 |

    Kurt is a researcher at the quantitative hedge fund PDT Partners. 253 |

    254 |
  • 255 | A photo of Kush R. Varshney 256 |
    257 |

    Kush R. Varshney 258 |

    Kush is a research staff member at IBM Research and a data ambassador with DataKind. 259 |

    260 |
261 |
    262 |
  • 263 | A photo of Brian Dalessandro 264 |
    265 |

    Brian d'Alessandro 266 |

    Brian is VP of Data Science at Dstillery and Adjunct Professor of Data Science at NYU Stern School of Business. 267 |

    268 |
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5px rgba(0,0,0,0.3) 162 | 163 | color: white 164 | 165 | &.selected 166 | border-bottom: none 167 | 168 | color: accent 169 | 170 | background: white 171 | 172 | &::before 173 | float: left 174 | 175 | display: inline-block 176 | width: nav-width 177 | height: nav-width 178 | margin: -10px 15px 0 -10px 179 | 180 | {font-medium} 181 | font-family: "Glyphicons Halflings" 182 | font-style: normal 183 | font-weight: normal 184 | letter-spacing: normal 185 | line-height: 60px 186 | text-align: center 187 | vertical-align: middle 188 | -webkit-font-smoothing: antialiased 189 | -moz-osx-font-smoothing: grayscale 190 | 191 | &[href="#home"]::before 192 | {icon-home} 193 | &[href="#about"]::before 194 | {icon-info-sign} 195 | &[href="#resources"]::before 196 | {icon-book} 197 | &[href="#lectures"]::before 198 | {icon-calendar} 199 | &[href="#assignments"]::before 200 | {icon-list} 201 | &[href="#project"]::before 202 | {icon-star} 203 | &[href="#people"]::before 204 | {icon-user} 205 | 206 | 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2 |
3 | {{#each PDF}} 4 | {{@key}} 5 | {{/each}} 6 | {{#each ZIP}} 7 | {{@key}} 8 | {{/each}} 9 |
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