├── .gitattributes ├── .gitignore ├── CODE-OF-CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE ├── README.md └── UNDER-REVIEW.md /.gitattributes: -------------------------------------------------------------------------------- 1 | * text=auto 2 | readme.md merge=union 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | README_files 2 | README.html -------------------------------------------------------------------------------- /CODE-OF-CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as 6 | contributors and maintainers pledge to making participation in our project and 7 | our community a harassment-free experience for everyone, regardless of age, body 8 | size, disability, ethnicity, gender identity and expression, level of experience, 9 | nationality, personal appearance, race, religion, or sexual identity and 10 | orientation. 11 | 12 | ## Our Standards 13 | 14 | Examples of behavior that contributes to creating a positive environment 15 | include: 16 | 17 | * Using welcoming and inclusive language 18 | * Being respectful of differing viewpoints and experiences 19 | * Gracefully accepting constructive criticism 20 | * Focusing on what is best for the community 21 | * Showing empathy towards other community members 22 | 23 | Examples of unacceptable behavior by participants include: 24 | 25 | * The use of sexualized language or imagery and unwelcome sexual attention or 26 | advances 27 | * Trolling, insulting/derogatory comments, and personal or political attacks 28 | * Public or private harassment 29 | * Publishing others' private information, such as a physical or electronic 30 | address, without explicit permission 31 | * Other conduct which could reasonably be considered inappropriate in a 32 | professional setting 33 | 34 | ## Our Responsibilities 35 | 36 | Project maintainers are responsible for clarifying the standards of acceptable 37 | behavior and are expected to take appropriate and fair corrective action in 38 | response to any instances of unacceptable behavior. 39 | 40 | Project maintainers have the right and responsibility to remove, edit, or 41 | reject comments, commits, code, wiki edits, issues, and other contributions 42 | that are not aligned to this Code of Conduct, or to ban temporarily or 43 | permanently any contributor for other behaviors that they deem inappropriate, 44 | threatening, offensive, or harmful. 45 | 46 | ## Scope 47 | 48 | This Code of Conduct applies both within project spaces and in public spaces 49 | when an individual is representing the project or its community. Examples of 50 | representing a project or community include using an official project e-mail 51 | address, posting via an official social media account, or acting as an appointed 52 | representative at an online or offline event. Representation of a project may be 53 | further defined and clarified by project maintainers. 54 | 55 | ## Enforcement 56 | 57 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 58 | reported by contacting the project team at <%= email %>. All 59 | complaints will be reviewed and investigated and will result in a response that 60 | is deemed necessary and appropriate to the circumstances. The project team is 61 | obligated to maintain confidentiality with regard to the reporter of an incident. 62 | Further details of specific enforcement policies may be posted separately. 63 | 64 | Project maintainers who do not follow or enforce the Code of Conduct in good 65 | faith may face temporary or permanent repercussions as determined by other 66 | members of the project's leadership. 67 | 68 | ## Attribution 69 | 70 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, 71 | available at [http://contributor-covenant.org/version/1/4][version] 72 | 73 | [homepage]: http://contributor-covenant.org 74 | [version]: http://contributor-covenant.org/version/1/4/ 75 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contribution Guidelines 2 | 3 | Please note that this project is released with a 4 | [Contributor Code of Conduct](code-of-conduct.md). By participating in this 5 | project you agree to abide by its terms. 6 | 7 | --- 8 | 9 | Ensure your pull request adheres to the following guidelines: 10 | 11 | - Make sure you use the MLA format 12 | - Make sure you do not use a broken link 13 | - Make sure your resource is high quality 14 | - Make sure your resource is not already on the list 15 | - Try to provide a link to free resources as much as possible 16 | 17 | Thank you for your suggestions! 18 | 19 | 20 | ## Updating your PR 21 | 22 | A lot of times, making a PR adhere to the standards above can be difficult. 23 | If the maintainers notice anything that we'd like changed, we'll ask you to 24 | edit your PR before we merge it. There's no need to open a new PR, just edit 25 | the existing one. 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For 392 | the avoidance of doubt, this paragraph does not form part of the 393 | public licenses. 394 | 395 | Creative Commons may be contacted at creativecommons.org. 396 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Decision Science [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) 2 | 3 | Une liste de ressources triées sur le volet à propos de tout ce qui touche à la prise de décision : vidéos, tutoriels, livres, documents, thèses, articles, datasets et libs open source. Cliquez sur le menu hamburger 🍔 pour naviguer plus facilement. 4 | 5 | 👍 Vous aimez ? Un like, un partage aiderait grandement le projet ! Partageons les connaissances ! 6 | 7 | ⚠️ Disclaimer : la quasi-totalité des ressources est dispo gratuitement et légalement. Je ne touche rien sur les ventes des rares ressources payantes, qui sont simplement référencées car j'estime que ce sont des ressources de valeur. 8 | 9 | ## Sommaire 10 | - [Awesome Decision Science ](#awesome-decision-science-) 11 | - [🤖 Artificial Intelligence, Computational Intelligence, and Machine Learning](#-artificial-intelligence-computational-intelligence-and-machine-learning) 12 | - [Books](#books) 13 | - [Computational Intelligence](#computational-intelligence) 14 | - [Deep Learning](#deep-learning) 15 | - [Explainable AI](#explainable-ai) 16 | - [Machine Learning](#machine-learning) 17 | - [Courses and lecture notes, posts](#courses-and-lecture-notes-posts) 18 | - [Deep Learning](#deep-learning-1) 19 | - [Explainable AI](#explainable-ai-1) 20 | - [Machine Learning](#machine-learning-1) 21 | - [Reinforcement Learning and Control Theory](#reinforcement-learning-and-control-theory) 22 | - [Datasets](#datasets) 23 | - [Packages](#packages) 24 | - [Data loading](#data-loading) 25 | - [Explainable AI](#explainable-ai-2) 26 | - [Feature Engineering](#feature-engineering) 27 | - [Hyperparameter optimization](#hyperparameter-optimization) 28 | - [Machine Learning techniques](#machine-learning-techniques) 29 | - [Papers](#papers) 30 | - [Deep Learning](#deep-learning-2) 31 | - [Bayesian approaches](#bayesian-approaches) 32 | - [Generative aspects](#generative-aspects) 33 | - [Mathematical aspects: approximation and generalization](#mathematical-aspects-approximation-and-generalization) 34 | - [Mathematical aspects: optimization](#mathematical-aspects-optimization) 35 | - [Machine Learning](#machine-learning-2) 36 | - [Conformal Prediction](#conformal-prediction) 37 | - [Explainable AI](#explainable-ai-3) 38 | - [Fuzzy sets](#fuzzy-sets) 39 | - [Imbalanced data problems](#imbalanced-data-problems) 40 | - [Training ML models](#training-ml-models) 41 | - [Posts and threads](#posts-and-threads) 42 | - [Explainable AI (XAI)](#explainable-ai-xai) 43 | - [Imbalanced data problems](#imbalanced-data-problems-1) 44 | - [Talks, conferences, and videos](#talks-conferences-and-videos) 45 | - [📊 Business Intelligence, Data Visualization, Communicating and Reporting](#-business-intelligence-data-visualization-communicating-and-reporting) 46 | - [Books](#books-1) 47 | - [Courses and lecture notes, posts](#courses-and-lecture-notes-posts-1) 48 | - [Datasets](#datasets-1) 49 | - [Packages](#packages-1) 50 | - [Data structures](#data-structures) 51 | - [Python](#python) 52 | - [Data Visualization and Reporting](#data-visualization-and-reporting) 53 | - [Julia](#julia) 54 | - [Python](#python-1) 55 | - [Papers](#papers-1) 56 | - [Posts and threads](#posts-and-threads-1) 57 | - [Talks, conferences, and videos](#talks-conferences-and-videos-1) 58 | - [💻 Computer Science and Software Engineering](#-computer-science-and-software-engineering) 59 | - [Books](#books-2) 60 | - [Algorithmics, data structures, and programming languages](#algorithmics-data-structures-and-programming-languages) 61 | - [Scientific programming](#scientific-programming) 62 | - [Software development](#software-development) 63 | - [Databases](#databases) 64 | - [Courses and lecture notes, posts](#courses-and-lecture-notes-posts-2) 65 | - [Algorithms](#algorithms) 66 | - [Scientific programming](#scientific-programming-1) 67 | - [Software engineering](#software-engineering) 68 | - [Packages](#packages-2) 69 | - [Python](#python-2) 70 | - [Data processing](#data-processing) 71 | - [GUI](#gui) 72 | - [Papers](#papers-2) 73 | - [Posts and threads](#posts-and-threads-2) 74 | - [Talks, conferences, and videos](#talks-conferences-and-videos-2) 75 | - [🗺️ Geospatial Analysis](#️-geospatial-analysis) 76 | - [Books](#books-3) 77 | - [Courses and lecture notes, posts](#courses-and-lecture-notes-posts-3) 78 | - [Datasets](#datasets-2) 79 | - [Packages](#packages-3) 80 | - [Papers](#papers-3) 81 | - [Posts and threads](#posts-and-threads-3) 82 | - [Talks, conferences, and videos](#talks-conferences-and-videos-3) 83 | - [👩‍🔬 Mathematics, Operations Research, Game Theory, and Simulations](#-mathematics-operations-research-game-theory-and-simulations) 84 | - [Books](#books-4) 85 | - [Algebra](#algebra) 86 | - [Applied Mathematics](#applied-mathematics) 87 | - [Game Theory and Simulations](#game-theory-and-simulations) 88 | - [Graph Theory](#graph-theory) 89 | - [Optimization](#optimization) 90 | - [Sequential Problems](#sequential-problems) 91 | - [Courses and lecture notes, posts](#courses-and-lecture-notes-posts-4) 92 | - [Mathematical Finance](#mathematical-finance) 93 | - [Probability](#probability) 94 | - [Datasets](#datasets-3) 95 | - [Packages](#packages-4) 96 | - [Optimization](#optimization-1) 97 | - [Sensitivity analysis](#sensitivity-analysis) 98 | - [Papers](#papers-4) 99 | - [Posts and threads](#posts-and-threads-4) 100 | - [Optimization](#optimization-2) 101 | - [Talks, conferences, and videos](#talks-conferences-and-videos-4) 102 | - [🤯 Methodology, interactions, and philosophical aspects of Science](#-methodology-interactions-and-philosophical-aspects-of-science) 103 | - [Building theories](#building-theories) 104 | - [Computational Science](#computational-science) 105 | - [Machine Learning and Statistics](#machine-learning-and-statistics) 106 | - [Mathematics](#mathematics) 107 | - [Scientific approaches](#scientific-approaches) 108 | - [📈 Statistics, Econometrics, and Data Mining](#-statistics-econometrics-and-data-mining) 109 | - [Books](#books-5) 110 | - [Clustering](#clustering) 111 | - [Econometrics](#econometrics) 112 | - [Statistics](#statistics) 113 | - [Bayesian Statistics](#bayesian-statistics) 114 | - [Exponential family](#exponential-family) 115 | - [Historical aspects](#historical-aspects) 116 | - [Inference and mathematical aspects](#inference-and-mathematical-aspects) 117 | - [Missing data](#missing-data) 118 | - [Regression modeling](#regression-modeling) 119 | - [Statistical software](#statistical-software) 120 | - [Time Series](#time-series) 121 | - [Courses and lecture notes, posts](#courses-and-lecture-notes-posts-5) 122 | - [Causal Inference](#causal-inference) 123 | - [Econometrics](#econometrics-1) 124 | - [Statistics \& Probability](#statistics--probability) 125 | - [Forecasting](#forecasting) 126 | - [Datasets](#datasets-4) 127 | - [Forecasting](#forecasting-1) 128 | - [Marketing applications](#marketing-applications) 129 | - [Packages](#packages-5) 130 | - [Python](#python-3) 131 | - [Time Series](#time-series-1) 132 | - [R](#r) 133 | - [Papers](#papers-5) 134 | - [Clustering](#clustering-1) 135 | - [Probabilistic Graphical Models and associated optimization techniques](#probabilistic-graphical-models-and-associated-optimization-techniques) 136 | - [Statistics](#statistics-1) 137 | - [Bayesian Statistics](#bayesian-statistics-1) 138 | - [Causality](#causality) 139 | - [Distributions](#distributions) 140 | - [Statistical hypothesis testing (NHST)](#statistical-hypothesis-testing-nhst) 141 | - [Posts and threads](#posts-and-threads-5) 142 | - [Bayesian Statistics](#bayesian-statistics-2) 143 | - [General topics](#general-topics) 144 | - [Variable selection / Feature selection](#variable-selection--feature-selection) 145 | - [Talks, conferences, and videos](#talks-conferences-and-videos-5) 146 | - [Bayesian Statistics](#bayesian-statistics-3) 147 | - [Stochastic Processes](#stochastic-processes) 148 | - [📄 Text Mining and Natural Language Processing](#-text-mining-and-natural-language-processing) 149 | - [Books](#books-6) 150 | - [Courses and lecture notes, posts](#courses-and-lecture-notes-posts-6) 151 | - [Datasets](#datasets-5) 152 | - [Packages](#packages-6) 153 | - [Papers](#papers-6) 154 | - [Posts and threads](#posts-and-threads-6) 155 | - [Talks, conferences, and videos](#talks-conferences-and-videos-6) 156 | 157 | ## 🤖 Artificial Intelligence, Computational Intelligence, and Machine Learning 158 | ### Books 159 | #### Computational Intelligence 160 | - Engelbrecht, Andries P. Computational intelligence: an introduction. John Wiley & Sons, 2007. [[Link]](https://onlinelibrary.wiley.com/doi/book/10.1002/9780470512517) 161 | #### Deep Learning 162 | - Bishop, Christopher M., and Hugh Bishop. "Deep learning: foundations and concepts." Springer, 2024. [[Link]](https://www.bishopbook.com/) 163 | - Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for machine learning. Cambridge University Press, 2020. [[Link]](https://mml-book.github.io/) 164 | - Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT Press, 2016. [[Link]](https://www.deeplearningbook.org/) 165 | - Grohs, Philipp, and Gitta Kutyniok, eds. Mathematical aspects of deep learning. Cambridge University Press, 2022. [[Link]](https://www.cambridge.org/core/books/mathematical-aspects-of-deep-learning/8D9B41D1E9BB8CA515E93412EECC2A7E) 166 | - Prince, Simon JD. Understanding Deep Learning. MIT press, 2023. [[Link]](https://udlbook.github.io/udlbook/) 167 | - Zhang, Aston, et al. Dive into deep learning. Cambridge University Press, 2023. [[Link]](https://d2l.ai/) 168 | #### Explainable AI 169 | - Biecek, Przemyslaw, and Tomasz Burzykowski. Explanatory model analysis: explore, explain, and examine predictive models. CRC Press, 2021. [[Link]](https://ema.drwhy.ai/) 170 | - Hall, Curtis and Pandey. Machine Learning for High-Risk Applications. O'Reilly, 2023. [[Link]](https://www.oreilly.com/library/view/machine-learning-for/9781098102425/) 171 | - Molnar, Christoph. Interpretable machine learning. Lulu. com, 2020. [[Link]](https://christophm.github.io/interpretable-ml-book/) 172 | #### Machine Learning 173 | - Bishop, Christopher M., and Nasser M. Nasrabadi. Pattern recognition and machine learning. Vol. 4. No. 4. New York: Springer, 2006. [[Link]](https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/) 174 | - Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for machine learning. Cambridge University Press, 2020. [[Link]](https://mml-book.github.io/) 175 | - Efron, Bradley, and Trevor Hastie. Computer age statistical inference, student edition: algorithms, evidence, and data science. Vol. 6. Cambridge University Press, 2021. [[Link]](https://hastie.su.domains/CASI/) 176 | - Hastie, Trevor, Robert Tibshirani, and Martin Wainwright. Statistical learning with sparsity: the lasso and generalizations. CRC press, 2015. [[Link]](https://hastie.su.domains/StatLearnSparsity/) 177 | - Huber, Martin. Causal analysis: Impact evaluation and Causal Machine Learning with applications in R. MIT Press, 2023. [[Link]](https://mitpress.ublish.com/ebook/causal-analysis-impact-evaluation-and-causal-machine-learning-with-applications-in-r-preview/12759/Cover) 178 | - James, G., Witten, D., Hastie, T., Tibshirani, R., Taylor, J. An Introduction to Statistical Learning: With Applications in Python; Springer: Berlin/Heidelberg, Germany, 2023. [[Link]](https://www.statlearning.com/) 179 | - Katsov, Ilya. Introduction to algorithmic marketing: Artificial intelligence for marketing operations. Grid Dynamics, 2017. [[Link]](https://www.algorithmicmarketingbook.com/) 180 | - MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003. [[Link]](https://inference.org.uk/itila/book.html) 181 | - Murphy, Kevin P. Probabilistic machine learning: Advanced topics. MIT Press, 2023. [[Link]](https://probml.github.io/pml-book/book2.html) 182 | - Murphy, Kevin P. Probabilistic machine learning: an introduction. MIT Press, 2022. [[Link]](https://probml.github.io/pml-book/book1.html) 183 | - Siddiqi, Naeem. Intelligent credit scoring: Building and implementing better credit risk scorecards. John Wiley & Sons, 2017. [[Link]](https://www.wiley.com/en-fr/Intelligent+Credit+Scoring%3A+Building+and+Implementing+Better+Credit+Risk+Scorecards%2C+2nd+Edition-p-9781119279150) 184 | ### Courses and lecture notes, posts 185 | #### Deep Learning 186 | - Lippe, Phillip. UvA Deep Learning Tutorials. 2022. [[Link]](https://uvadlc-notebooks.readthedocs.io/en/latest/) 187 | - Ollion, Charles, and Olivier Grisel. Deep Learning course: lecture slides and lab notebooks. Institut Polytechnique de Paris, 2017. [[Link]](https://m2dsupsdlclass.github.io/lectures-labs/) 188 | #### Explainable AI 189 | - Galli, Soledad. Interpreting Machine Learning Models [[Link]](https://www.trainindata.com/p/machine-learning-interpretability) 190 | - Lakkaraju, Hima, et al. Explainable Artificial Intelligence: From Simple Predictors to Complex Generative Models. Harvard University, 2023. [[Link]](https://interpretable-ml-class.github.io/) 191 | #### Machine Learning 192 | - Christensen, Henrik I. Support Vector Machines - SVM & RVM. Georgia Insitute of Technology. [[Link]](https://faculty.cc.gatech.edu/~hic/CS7616/pdf/lecture9.pdf) 193 | - Inria. Machine learning in Python with scikit-learn. FUN, 2023. [[Link]](https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/) 194 | - MLU-Explain Team. MLU-Explain. Amazon (2021). [[Link]](https://mlu-explain.github.io/) 195 | #### Reinforcement Learning and Control Theory 196 | - Dimitry Bertsekas. Reinforcement Learning and Optimal Control. [[Link]](http://www.mit.edu/~dimitrib/RLbook.html) 197 | - Elad Hazan, Karan Singh. Introduction to Online Nonstochastic Control. [[Link]](https://arxiv.org/abs/2211.09619) 198 | ### Datasets 199 | - Andreas Luttens, et al. Large-scale Docking Datasets for Machine Learning. 2, Zenodo, 8 May 2023. [[Link]](https://zenodo.org/records/7953917) 200 | - Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and Jason H. Moore (2017). PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Mining 10, page 36. [[Paper]](https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0154-4) [[Code]](https://github.com/EpistasisLab/pmlb) 201 | ### Packages 202 | #### Data loading 203 | - mlx-data. Efficient framework-agnostic data loading. Apple, 2023. [[Link]](https://github.com/ml-explore/mlx-data) 204 | #### Explainable AI 205 | - Alibi explain. Open-source interpretability library supporting black box, white box, global and local interpratability methods. [[Link]](https://docs.seldon.io/projects/alibi) 206 | - Dalex. Responsible Machine Learning in Python. [[Link]](https://dalex.drwhy.ai/python/) 207 | - Scikit-explain. User-friendly Python module for machine learning explainability with a comprehensive toolset of interpretability methods. [[Link]](https://scikit-explain.readthedocs.io/) 208 | - Shapash. Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models. MAIF, 2021.[[Link]](https://maif.github.io/shapash/) 209 | - Sudjianto, Agus, et al. "PiML Toolbox for Interpretable Machine Learning Model Development and Validation." arXiv preprint arXiv:2305.04214 210 | #### Feature Engineering 211 | - Feature_engine. Feature engineering package with sklearn like functionality. [[Link]](https://feature-engine.trainindata.com/en/latest/) 212 | #### Hyperparameter optimization 213 | - Optuna. A hyperparameter optimization framework. [[Link]](https://optuna.org/) 214 | #### Machine Learning techniques 215 | - Catboost. A fast, scalable, high-performance Gradient Boosting on Decision Trees library used for ranking, classification, regression, and other machine learning tasks for Python, R, Java, and C++. Supports computation on CPU and GPU. [[Link]](https://catboost.ai/) 216 | - Khuat, Thanh Tung, and Bogdan Gabrys. "hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms." arXiv preprint arXiv:2210.02704 (2022). [[Link]](https://arxiv.org/abs/2210.02704) 217 | - quantile-forest. Quantile Regression Forests compatible with scikit-learn. [[Link]](https://zillow.github.io/quantile-forest/) 218 | ### Papers 219 | #### Deep Learning 220 | ##### Bayesian approaches 221 | - Arbel, Julyan, et al. A Primer on Bayesian Neural Networks: Review and Debates. arXiv preprint arXiv:2309.16314 (2023). [[Link]](https://arxiv.org/abs/2309.16314) 222 | - Hellström, Fredrik, et al. Generalization bounds: perspectives from information theory and PAC-Bayes. arXiv preprint arXiv:2309.04381 (2023). [[Link]](https://arxiv.org/abs/2309.04381) 223 | - Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013). [[Link]](https://arxiv.org/abs/1312.6114) 224 | - Nalisnick, Eric, and Padhraic Smyth. "Stick-breaking variational autoencoders." arXiv preprint arXiv:1605.06197 (2016). [[Link]](https://arxiv.org/abs/1605.06197) 225 | ##### Generative aspects 226 | - Coste, Simon. Diffusion. University of Paris, 2023. [[Link]](https://scoste.fr/posts/diffusion/) 227 | - Galerne, Bruno, and Valentin De Bortoli. Generative Modelling. ENS Paris-Saclay, 2023. [[Link]](https://vdeborto.github.io/project/generative_modeling/) 228 | ##### Mathematical aspects: approximation and generalization 229 | - Bartlett, Peter L., Andrea Montanari, and Alexander Rakhlin. Deep learning: a statistical viewpoint. Acta numerica 30 (2021): 87-201. [[Link]](https://arxiv.org/abs/2103.09177) 230 | - Berner, Julius, et al. The modern mathematics of deep learning. arXiv preprint arXiv:2105.04026 (2021): 86-114. [[Link]](https://arxiv.org/abs/2105.04026) 231 | - DeVore, Ronald, Boris Hanin, and Guergana Petrova. Neural network approximation. Acta Numerica 30 (2021): 327-444. [[Link]](https://arxiv.org/abs/2012.14501) 232 | - Jacot, Arthur, Franck Gabriel, and Clément Hongler. "Neural tangent kernel: Convergence and generalization in neural networks." Advances in neural information processing systems 31 (2018). [[Link]](https://arxiv.org/abs/1806.07572) 233 | - Hornik, Kurt. "Approximation capabilities of multilayer feedforward networks." Neural networks 4.2 (1991): 251-257. [[Link]](https://web.njit.edu/~usman/courses/cs677_spring21/hornik-nn-1991.pdf) 234 | - Hornik, Kurt, Maxwell Stinchcombe, and Halbert White. "Multilayer feedforward networks are universal approximators." Neural networks 2.5 (1989): 359-366. [[Link]](https://cognitivemedium.com/magic_paper/assets/Hornik.pdf) 235 | - Petersen, Philipp Christian. Neural network theory. University of Vienna 535 (2020). [[Link]](http://pc-petersen.eu/Neural_Network_Theory.pdf) 236 | ##### Mathematical aspects: optimization 237 | - Khaled, Ahmed, and Peter Richtárik. "Better theory for SGD in the nonconvex world." arXiv preprint arXiv:2002.03329 (2020). [[Link]](https://arxiv.org/abs/2002.03329) 238 | - Sun, Ruoyu. Optimization for deep learning: theory and algorithms. arXiv preprint arXiv:1912.08957 (2019). [[Link]](https://arxiv.org/abs/1912.08957) 239 | #### Machine Learning 240 | ##### Conformal Prediction 241 | - Angelopoulos, Anastasios N., and Stephen Bates. "A gentle introduction to conformal prediction and distribution-free uncertainty quantification." arXiv preprint arXiv:2107.07511 (2021). [[Link]](https://arxiv.org/abs/2107.07511) 242 | - Fontana, Matteo, Gianluca Zeni, and Simone Vantini. "Conformal prediction: a unified review of theory and new challenges." arXiv preprint arXiv:2005.07972 (2020). [[Link]](https://arxiv.org/abs/2005.07972) 243 | ##### Explainable AI 244 | - Bilodeau, Blair, et al. "Impossibility theorems for feature attribution." Proceedings of the National Academy of Sciences 121.2 (2024): e2304406120. [[Link]](https://arxiv.org/abs/2212.11870) 245 | - Ibrahim Amoukou, Salim. Trustworthy machine learning: explainability and distribution-free uncertainty quantification. Diss. université Paris-Saclay, 2023. [[Link]](https://www.biblio.univ-evry.fr/theses/2023/2023UPASM034.pdf) 246 | - Huang, Xuanxiang, and Joao Marques-Silva. "The inadequacy of Shapley values for explainability." arXiv preprint arXiv:2302.08160 (2023). 247 | (2023). [[Link]](https://arxiv.org/abs/2302.08160) 248 | ##### Fuzzy sets 249 | - Khuat, Thanh Tung, Dymitr Ruta, and Bogdan Gabrys. "Hyperbox-based machine learning algorithms: a comprehensive survey." Soft Computing 25.2 (2021): 1325-1363. [[Link]](https://arxiv.org/abs/1901.11303) 250 | ##### Imbalanced data problems 251 | - Elor, Yotam, and Hadar Averbuch-Elor. "To SMOTE, or not to SMOTE?." arXiv preprint arXiv:2201.08528 (2022). [[Link]](https://arxiv.org/abs/2201.08528) 252 | - van den Goorbergh, Ruben, et al. "The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression." Journal of the American Medical Informatics Association 29.9 (2022): 1525-1534. [[Link]](https://academic.oup.com/jamia/article/29/9/1525/6605096) 253 | ##### Training ML models 254 | - Mirzasoleiman, Baharan, Jeff Bilmes, and Jure Leskovec. "Coresets for data-efficient training of machine learning models." International Conference on Machine Learning. PMLR, 2020. [[Link]](https://proceedings.mlr.press/v119/mirzasoleiman20a.html) 255 | ### Posts and threads 256 | #### Explainable AI (XAI) 257 | - Of Models and Meanings. SHAP is the Blockchain of xAI. Of Models and Meanings, 2022. [[Link]](https://modelmeanings.wordpress.com/2022/05/12/shap-is-the-blockchain-of-xai/) 258 | - Of Models and Meanings. What You Could Do with the Shapley Computation. Of Models and Meanings, 2022. [[Link]](https://modelmeanings.wordpress.com/2022/09/04/what-you-could-do-with-the-shapley-computation/) 259 | #### Imbalanced data problems 260 | - Mougan, Carl. Why SMOTE is not used in prize-winning Kaggle solutions?. Data Science, 2021. [[Link]](https://datascience.stackexchange.com/questions/106461/why-smote-is-not-used-in-prize-winning-kaggle-solutions) 261 | ### Talks, conferences, and videos 262 | - Dieng, Adji B. Learning From Data: The Two Cultures. Association for Computing Machinery, 2021. [[Link]](https://youtu.be/JJnTLNoNTME?si=uiuHKCcPFhF7sz6h) 263 | - Rich, DJ. Mutual Information. True Theta LLC, 2020. [[Link]](https://www.youtube.com/@Mutual_Information) 264 | 265 | ## 📊 Business Intelligence, Data Visualization, Communicating and Reporting 266 | ### Books 267 | - Duarte, Nancy. Resonate: Present visual stories that transform audiences. John Wiley & Sons, 2013. [[Link]](https://www.duarte.com/resources/books/resonate/) 268 | - Duarte, Nancy. Slide: ology: The art and science of creating great presentations. Vol. 1. Sebastapol: O'Reilly Media, 2008. [[Link]](https://www.duarte.com/resources/books/slideology/) 269 | - Knaflic, Cole Nussbaumer. Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons, 2015. [[Link]](https://www.storytellingwithdata.com/books) 270 | - Knaflic, Cole Nussbaumer. Storytelling with data: let's practice!. John Wiley & Sons, 2019. [[Link]](https://www.storytellingwithdata.com/books) 271 | - Wexler, Steve, Jeffrey Shaffer, and Andy Cotgreave. The big book of dashboards: visualizing your data using real-world business scenarios. John Wiley & Sons, 2017. [[Link]](https://www.bigbookofdashboards.com/) 272 | - Wilke, Claus O. Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media, 2019. [[Link]](https://clauswilke.com/dataviz/) 273 | ### Courses and lecture notes, posts 274 | ### Datasets 275 | ### Packages 276 | #### Data structures 277 | ##### Python 278 | - Polars. Dataframes powered by a multithreaded, vectorized query engine, written in Rust. [[Link]](https://www.pola.rs/) 279 | #### Data Visualization and Reporting 280 | ##### Julia 281 | - Genie. 🧞The highly productive Julia web framework. [[Link]](https://genieframework.com/) 282 | ###### Python 283 | - Marimo. marimo is an open-source reactive notebook for Python — reproducible, git-friendly, executable as a script, and shareable as an app. [[Link]](https://marimo.io/) 284 | - PyGWalker. Turn your pandas dataframe into an interactive UI for visual analysis. [[Link]](https://docs.kanaries.net/pygwalker) 285 | - Streamlit. A faster way to build and share data apps. [[Link]](https://streamlit.io/) 286 | - Vizro. Vizro is a toolkit for creating modular data visualization applications. [[Link]](https://github.com/mckinsey/vizro) 287 | ## Papers 288 | ### Posts and threads 289 | ### Talks, conferences, and videos 290 | 291 | ## 💻 Computer Science and Software Engineering 292 | ### Books 293 | #### Algorithmics, data structures, and programming languages 294 | - Downey, Allen. Think complexity: complexity science and computational modeling. " O'Reilly Media, Inc.", 2018. [[Link]](https://www.greenteapress.com/complexity/) 295 | - Downey, Allen. Think data structures: algorithms and information retrieval in Java. " O'Reilly Media, Inc.", 2017. [[Link]](https://greenteapress.com/wp/think-data-structures/) 296 | - Downey, Allen. Think Python. " O'Reilly Media, Inc.", 2012. [[Link]](https://greenteapress.com/wp/think-python-2e/) 297 | - Johnston, Nathaniel, and Dave Greene. Conway's Game of Life: Mathematics and Construction. Self-published, 2022. [[Link]](https://conwaylife.com/book/) 298 | - Miller, Brad, and David Ranum. Problem-solving with algorithms and data structures. University of Auckland, 2013. [[Link]](https://dlib.hust.edu.vn/bitstream/HUST/17856/1/OER000000244.pdf) [[Website]](https://www.openbookproject.net/books/pythonds/index.html) 299 | - Nipkow, Tobias. "Functional Data Structures and Algorithms A Proof Assistant Approach." (2023). [[Link]](https://functional-algorithms-verified.org/) 300 | #### Scientific programming 301 | - Blondel, Mathieu, and Vincent Roulet. "The Elements of Differentiable Programming." arXiv preprint arXiv:2403.14606 (2024). [[Link]](https://arxiv.org/abs/2403.14606) 302 | #### Software development 303 | - Chacon, Scott, and Ben Straub. Pro git. Springer Nature, 2014. [[Link]](https://git-scm.com/book/en/v2) 304 | #### Databases 305 | - Petrov, Alex. Database Internals: A deep dive into how distributed data systems work. O'Reilly Media, 2019. [[Link]](https://www.databass.dev/) 306 | ### Courses and lecture notes, posts 307 | #### Algorithms 308 | - Roughgarden, Tim. Lecture Notes. Columbia University. [[Link]](https://timroughgarden.org/notes.html) 309 | #### Scientific programming 310 | - Raschka, Sebastian. Scientific Computing in Python: Introduction to NumPy and Matplotlib. sebastianraschka.com, 2020. [[Link]](https://sebastianraschka.com/blog/2020/numpy-intro.html) 311 | #### Software engineering 312 | - Atlassian. Gitflow workflow. [[Link]](https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow) 313 | - Atlassian. Trunk-based development. [[Link]](Trunk-based development) 314 | - Shvets, Alexander. Refactoring Guru. 2014. [[Link]](https://refactoring.guru/) 315 | ### Packages 316 | #### Python 317 | ##### Data processing 318 | - Bytewax. Python Stream Processing. [[Link]](https://bytewax.io/) 319 | ##### GUI 320 | - Textual. The lean application framework for Python. Build sophisticated user interfaces with a simple Python API. Run your apps in the terminal and a web browser. [[Link]](https://github.com/Textualize/textual) 321 | ### Papers 322 | ### Posts and threads 323 | ### Talks, conferences, and videos 324 | 325 | ## 🗺️ Geospatial Analysis 326 | ### Books 327 | - Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow. Geocomputation with R. CRC Press, 2019. [[Link]](https://r.geocompx.org/) 328 | - Moraga, Paula. Geospatial health data: Modeling and visualization with R-INLA and shiny. CRC Press, 2019. [[Link]](https://www.paulamoraga.com/book-geospatial/) 329 | - Moraga, Paula. Spatial Statistics for Data Science: Theory and Practice with R. CRC Press, 2023. [[Link]](https://www.paulamoraga.com/book-spatial/) 330 | ### Courses and lecture notes, posts 331 | ### Datasets 332 | ### Packages 333 | ### Papers 334 | ### Posts and threads 335 | ### Talks, conferences, and videos 336 | 337 | ## 👩‍🔬 Mathematics, Operations Research, Game Theory, and Simulations 338 | ### Books 339 | #### Algebra 340 | - Axler, Sheldon. Linear algebra done right. Springer Nature, 2023. [[Link]](https://linear.axler.net/) 341 | #### Applied Mathematics 342 | - Isoz, Vincent. Opera Magistris (Elements of Applied Mathematics). Sciences.ch, 2016. [[Link]](https://archive.org/details/OperaMagistris) 343 | #### Game Theory and Simulations 344 | - Downey, Allen B. Modeling and Simulation in Python: An Introduction for Scientists and Engineers. No Starch Press, 2023. [[Link]](https://greenteapress.com/wp/modsimpy/) 345 | #### Graph Theory 346 | - McNulty, Keith. Handbook of graphs and networks in people analytics: with examples in R and Python. CRC Press, 2022. [[Link]](https://ona-book.org/index.html) 347 | - Sargent, Thomas J., and John Stachurski. Economic Networks: Theory and Computation. QuantEcon, 2022. [[Link]](https://networks.quantecon.org/) 348 | #### Optimization 349 | - Boumal, Nicolas. An Introduction to Optimization on Smooth Manifolds. Cambridge University Press, 2023. [[Link]](https://www.nicolasboumal.net/book/) 350 | - Boyd, Stephen P., and Lieven Vandenberghe. Convex optimization. Cambridge University Press, 2004. [[Link]](https://web.stanford.edu/~boyd/cvxbook/) 351 | - Kwon, Changhyun. Julia Programming for Operations Research. Changhyun Kwon, 2019. [[Link]](https://juliabook.chkwon.net/book/frontmatter) 352 | - Martins, J. R. R. A. and Ning, A., Engineering Design Optimization, Cambridge University Press, 2022. [[Link]](https://mdobook.github.io/) 353 | - Nesterov, Yurii. Lectures on convex optimization. Vol. 137. Berlin: Springer, 2018. [[Link]](https://link.springer.com/book/10.1007/978-3-319-91578-4) 354 | - Sargent, Thomas J., and John Stachurski. Dynamic Programming Volume 1. QuantEcon, 2023. [[Link]](https://dp.quantecon.org/) 355 | #### Sequential Problems 356 | - Powell, Warren B. Sequential decision analytics and modeling: modeling with Python. Now, 2022. [[Link]](https://castle.princeton.edu/sdamodeling/) 357 | ### Courses and lecture notes, posts 358 | #### Mathematical Finance 359 | - Kempthorne, Peter, et al. "Topics in mathematics with applications in finance." Massachusetts Institute of Technology: MIT OpenCouseWare, 2013. [[Link]](https://ocw.mit.edu/courses/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/) 360 | - Roncalli, Thierry, Course 2023-2024 in Portfolio Allocation and Asset Management. SSRN, 2024. [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4698165&s=09) 361 | #### Probability 362 | - Arya, Nisha. Learn Probability in Computer Science with Stanford University for FREE. KDNuggets, 2023. [[Link]](https://www.kdnuggets.com/learn-probability-in-computer-science-with-stanford-university-for-free) 363 | ### Datasets 364 | ### Packages 365 | #### Optimization 366 | - Diamond, Steven, and Stephen Boyd. "CVXPY: A Python-embedded modeling language for convex optimization." Journal of Machine Learning Research 17.83 (2016): 1-5. [[Link to the paper]](https://www.jmlr.org/papers/v17/15-408.html) [[Link to the package]](https://www.cvxpy.org/) 367 | - PyPortfolioOpt. Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity. [[Link]](https://github.com/robertmartin8/PyPortfolioOpt) 368 | - scikit-portfolio. A portfolio optimization tool with scikit-learn interface. Hyperparameters selection and easy plotting of efficient frontiers. [[Link]](https://scikit-portfolio.github.io/scikit-portfolio/) 369 | #### Sensitivity analysis 370 | - SALib. Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods. [[Link]](https://github.com/SALib/SALib) 371 | ### Papers 372 | ### Posts and threads 373 | #### Optimization 374 | - Jones, Andy. Natural gradients. Andy Jones. [[Link]](https://andrewcharlesjones.github.io/journal/natural-gradients.html) 375 | ### Talks, conferences, and videos 376 | - MATLAB. Why Padé Approximations Are Great! | Control Systems in Practice. YouTube, 2022. [[Link]](https://www.youtube.com/watch?v=3TK8Fi_I0h0) 377 | 378 | ## 🤯 Methodology, interactions, and philosophical aspects of Science 379 | ### Building theories 380 | - Jaccard, James, and Jacob Jacoby. Theory construction and model-building skills: A practical guide for social scientists. Guilford publications, 2019. [[Link]](https://www.guilford.com/books/Theory-Construction-and-Model-Building-Skills/Jaccard-Jacoby/9781462542437) [[Website]](https://www.theory-construction.com/Main.html) 381 | ### Computational Science 382 | - Judd, Kenneth. The Potential Partnership Between Economics and Computational Science. PyData Chicago, 2021. [[Link]](https://youtu.be/wcEktEN52tY?si=jr83xLEFTY7-5fRp) 383 | ### Machine Learning and Statistics 384 | - Breiman, Leo. "Statistical modeling: The two cultures (with comments and a rejoinder by the author)." Statistical science 16.3 (2001): 199-231. [[Link]](https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.pdf) 385 | - Harrell, Frank. "Classification vs. Prediction". Statistical Thinking, 2017. [[Link]](https://www.fharrell.com/post/classification/) 386 | ### Mathematics 387 | - Polya, George. How to solve it: A new aspect of mathematical method. Vol. 85. Princeton university press, 2004. [[Link]](https://press.princeton.edu/books/paperback/9780691164076/how-to-solve-it) 388 | ### Scientific approaches 389 | - Wolfram, Stephen. A new kind of science. Vol. 5. Champaign, IL: Wolfram media, 2002. [[Link]](https://www.wolframscience.com/nks/) 390 | 391 | ## 📈 Statistics, Econometrics, and Data Mining 392 | ### Books 393 | #### Clustering 394 | - Govaert, Gérard, and Mohamed Nadif. Co-clustering: models, algorithms and applications. John Wiley & Sons, 2013. [[Link]](https://www.amazon.fr/Co-Clustering-G%C3%A9rard-Govaert/dp/1848214731/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=1697294646&sr=8-1) 395 | - Scrucca, Luca, et al. Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman and Hall/CRC, 2023. [[Link]](https://www.taylorfrancis.com/books/mono/10.1201/9781003277965/model-based-clustering-classification-density-estimation-using-mclust-luca-scrucca-chris-fraley-brendan-murphy-adrian-raftery) 396 | #### Econometrics 397 | - Ding, Peng. "Linear Model and Extensions." arXiv preprint arXiv:2401.00649 (2024). [[Link]](https://arxiv.org/abs/2401.00649) 398 | - Evans, Richard W., Computational Methods for Economists using Python, Open access Jupyter Book, v#.#.#, 2023. [[Link]](https://opensourceecon.github.io/CompMethods) 399 | - Wooldridge, Jeffrey M.. Introductory Econometrics: A Modern Approach. Brésil, Cengage Learning, 2020. [[Link]](https://www.amazon.fr/Introductory-Econometrics-Approach-Jeffrey-Wooldridge/dp/1337558869/ref=sr_1_1?crid=1B5NKJCMW9EM0&keywords=econometrics+a+modern+approach&psr=PDAY&qid=1696924730&s=pbdd&sprefix=econmetrics+a+modern+approach%2Cpbdd%2C94&sr=1-1) 400 | #### Statistics 401 | ##### Bayesian Statistics 402 | - Martin, Osvaldo A., Ravin Kumar, and Junpeng Lao. Bayesian modeling and computation in Python. CRC Press, 2021. [[Link]](https://bayesiancomputationbook.com/welcome.html) 403 | - McElreath, Richard. Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC, 2020. [[Link]](https://xcelab.net/rm/statistical-rethinking/) 404 | ##### Exponential family 405 | - Agresti, Alan. Categorical data analysis. Vol. 792. John Wiley & Sons, 2012. [[Link]](https://www.wiley.com/en-us/Categorical+Data+Analysis%2C+3rd+Edition-p-9781118710944) 406 | - Efron, Bradley. Exponential families in theory and practice. Cambridge University Press, 2022. [[Link]](https://www.cs.columbia.edu/~blei/fogm/2018F/materials/Efron2018.pdf) 407 | ##### Historical aspects 408 | - Fischer, Hans. A history of the central limit theorem: from classical to modern probability theory. Vol. 4. New York: Springer, 2011. [[Link]](https://www.medicine.mcgill.ca/epidemiology/hanley/bios601/GaussianModel/HistoryCentralLimitTheorem.pdf) 409 | ##### Inference and mathematical aspects 410 | - Soch, Joram, et al. StatProofBook/StatProofBook.Github.Io: StatProofBook 2021. 2021, Zenodo, 2022. [[Link]](https://statproofbook.github.io/) 411 | - Wasserman, Larry. All of nonparametric statistics. Springer Science & Business Media, 2006. [[Link]](https://link.springer.com/book/10.1007/0-387-30623-4) 412 | - Wasserman, Larry. All of statistics: a concise course in statistical inference. Vol. 26. New York: Springer, 2004. [[Link]](https://www.stat.cmu.edu/~larry/all-of-statistics/) 413 | ##### Missing data 414 | - Van Buuren, Stef. Flexible imputation of missing data. CRC Press, 2018. [[Link]](https://stefvanbuuren.name/fimd/) 415 | ##### Regression modeling 416 | - McNulty, Keith. Handbook of regression modeling in people analytics: with examples in R and Python. CRC Press, 2021. [[Link]](https://peopleanalytics-regression-book.org/index.html) 417 | ##### Statistical software 418 | - Kuhn, Max, and Julia Silge. Tidy modeling with R. " O'Reilly Media, Inc.", 2022. [[Link]](https://www.tmwr.org/index.html) 419 | - Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science. " O'Reilly Media, Inc.". [[Link]](https://r4ds.hadley.nz/) 420 | #### Time Series 421 | - Cochrane, John H. "Time series for macroeconomics and finance." (1997). [[Link]](https://static1.squarespace.com/static/5e6033a4ea02d801f37e15bb/t/5eea91f725f16202da96235d/1592431098027/time_series_book.pdf) 422 | - Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. [[Link]](https://otexts.com/fpp3/) 423 | - Neusser, Klaus. Time series econometrics. Springer publication, 2016. [[Link]](https://www.amazon.fr/Time-Econometrics-Klaus-Neusser-ebook/dp/B01H30JHNA/ref=sr_1_1?crid=1QZFENEHXT9DG&keywords=Neusser%2C+Klaus.+Time+series+econometrics&qid=1696924805&sprefix=neusser%2C+klaus.+time+series+econometrics%2Caps%2C92&sr=8-1) 424 | ### Courses and lecture notes, posts 425 | #### Causal Inference 426 | - Cunningham, Scott et al. Mixtape Sessions: Causal Inference. 2022. [[Link]](https://github.com/Mixtape-Sessions/) 427 | - Ding, Peng. "A First Course in Causal Inference." arXiv preprint arXiv:2305.18793 (2023). [[Link]](https://arxiv.org/abs/2305.18793) 428 | #### Econometrics 429 | - Canay, Ivan. Econ 480-3 - Introduction to Econometrics. Northwestern University, 2021. [[Link]](https://sites.northwestern.edu/iac879/teaching/e-lectures-econ480/) 430 | - De Haan, Monique. ECON4150 - Introductory Econometrics. University of Oslo, 2018. [[Link]](https://www.uio.no/studier/emner/sv/oekonomi/ECON4150/v18/) 431 | #### Statistics & Probability 432 | - Dunn, Peter  K. The Theory of Distributions, 2023. [[Link]](https://bookdown.org/pkaldunn/DistTheory/) 433 | - Kozyrkov, Cassie. Statistical Thinking. YouTube, 2019. [[Link]](https://www.youtube.com/playlist?list=PLRKtJ4IpxJpBxX2S9wXJUhB1_ha3ADFpF) 434 | - Kunin, Daniel, et al. Seeing Theory. Brown University, 2016. [[Link]](https://seeing-theory.brown.edu/) 435 | #### Forecasting 436 | - Manani, Galli. Feature Engineering for Time Series Forecasting, 2022. [[Link]](https://www.trainindata.com/p/feature-engineering-for-forecasting) 437 | ### Datasets 438 | #### Forecasting 439 | - Godahewa, Rakshitha, et al. "Monash time series forecasting archive." arXiv preprint arXiv:2105.06643 (2021). [[Link]](https://arxiv.org/abs/2105.06643) 440 | - Lotsa Data. Salesforce, Hugging Face (2024). [[Link]](https://huggingface.co/datasets/Salesforce/lotsa_data) 441 | #### Marketing applications 442 | - "6 Free, High-Quality, Marketing Mix Modeling Datasets | Forecastegy." Web. 10/14/2023 [[Link]](https://forecastegy.com/posts/free-high-quality-marketing-mix-modeling-datasets) 443 | - Gaël Bernard and Periklis Andritsos. Datasets Simulating Customer Journeys. [[Link]](https://customer-journey.me/datasets/) 444 | ### Packages 445 | #### Python 446 | ##### Time Series 447 | - Alexandrov, Alexander, et al. "Gluonts: Probabilistic and neural time series modeling in python." The Journal of Machine Learning Research 21.1 (2020): 4629-4634. [[Link]](https://ts.gluon.ai/stable/) 448 | - Salvador, Stan, and Philip Chan. "Toward accurate dynamic time warping in linear time and space." Intelligent Data Analysis 11.5 (2007): 561-580. [[Link]](https://github.com/slaypni/fastdtw) 449 | - Fold. Fast Adaptive Time Series ML Engine. [[Link]](https://dream-faster.github.io/fold/) 450 | - Functime. Time-series machine learning at scale. Built on Polars for embarrassingly parallel feature engineering and forecasts. [[Link]](https://github.com/neocortexdb/functime) 451 | - HierarchicalForecast. Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods. [[Link]](https://nixtla.github.io/hierarchicalforecast/) 452 | - MFLES. A Specific implementation from ThymeBoost written with the help of Numba. [[Link]](https://github.com/tblume1992/MFLES) 453 | - mlforecast. Scalable machine 🤖 learning for time series forecasting. [[Link]](https://nixtla.github.io/mlforecast/) 454 | - NeuralForecast. Scalable and user-friendly neural 🧠 forecasting algorithms. [[Link]](https://nixtla.github.io/neuralforecast/) 455 | - SKForecast. Simplifies using sklearn models to do single and multistep forecasting and backtesting. [[Link]](https://skforecast.org) 456 | - StatsForecast. Lightning ⚡️ fast forecasting with statistical and econometric models. [[Link]](https://nixtla.github.io/statsforecast/) 457 | - ThymeBoost. Forecasting with Gradient Boosted Time Series Decomposition. [[Link]](https://github.com/tblume1992/ThymeBoost) 458 | - vectorbt. Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research. [[Link]](https://github.com/polakowo/vectorbt) 459 | #### R 460 | - Ross, Gordon J., and Dean Markwick. "dirichletprocess: An R package for fitting complex Bayesian nonparametric models." (2018). [[Link]](https://cloud.r-project.org/web/packages/dirichletprocess/vignettes/dirichletprocess.pdf) 461 | - van Buuren, S., and K. Groothuis-Oudshoorn. “Mice: Multivariate Imputation by Chained Equations in R”. Journal of Statistical Software, vol. 45, no. 3, Dec. 2011, pp. 1-67, doi:10.18637/jss.v045.i03. [[Paper]](https://www.jstatsoft.org/article/view/v045i03) [[Package]](https://cran.r-project.org/web/packages/mice/index.html) 462 | ### Papers 463 | #### Clustering 464 | - Keribin, Christine, Gilles Celeux, and Valérie Robert. "The latent block model: a useful model for high dimensional data." ISI 2017-61st world statistics congress. 2017. [[Link]](https://inria.hal.science/hal-01658589/) 465 | - Pham, Tung, et al. "Fast support vector clustering." Vietnam Journal of Computer Science 4 (2017): 13-21. [[Link]](https://link.springer.com/article/10.1007/s40595-016-0068-y) 466 | - Pham, Tung, Trung Le, and Hang Dang. "Scalable support vector clustering using budget." arXiv preprint arXiv:1709.06444 (2017). 467 | 468 | #### Probabilistic Graphical Models and associated optimization techniques 469 | - Blei, David M. Build, compute, critique, repeat: Data analysis with latent variable models. Annual Review of Statistics and Its Application 1 (2014): 203-232. [[Link]](https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-022513-115657) 470 | - Blei, David M., Alp Kucukelbir, and Jon D. McAuliffe. "Variational inference: A review for statisticians." Journal of the American Statistical Association 112.518 (2017): 859-877. [[Link]](https://arxiv.org/pdf/1601.00670) 471 | - Dieng, Adji Bousso. Deep Probabilistic Graphical Modeling. Columbia University, 2020. [[Link]](https://arxiv.org/abs/2104.12053) 472 | - Figurnov, Mikhail, Shakir Mohamed, and Andriy Mnih. "Implicit reparameterization gradients." Advances in neural information processing systems 31 (2018). [[Link]](https://proceedings.neurips.cc/paper_files/paper/2018/file/92c8c96e4c37100777c7190b76d28233-Paper.pdf) 473 | - Gelman, Andrew, Xiao-Li Meng, and Hal Stern. "Posterior predictive assessment of model fitness via realized discrepancies." Statistica sinica (1996): 733-760. [[Link]](https://www.jstor.org/stable/24306036) 474 | - Kim, Kyurae, et al. "Black-Box Variational Inference Converges." arXiv preprint arXiv:2305.15349 (2023). [[Link]](https://arxiv.org/abs/2305.15349) 475 | #### Statistics 476 | ##### Bayesian Statistics 477 | - Clarke, Bertrand, and Yuling Yao. "A Cheat Sheet for Bayesian Prediction." arXiv preprint arXiv:2304.12218 (2023). [[Link]](https://arxiv.org/abs/2304.12218) 478 | ##### Causality 479 | - Assaad, Charles K., Emilie Devijver, and Eric Gaussier. "Survey and evaluation of causal discovery methods for time series." Journal of Artificial Intelligence Research 73 (2022): 767-819. [[Link]](https://www.jair.org/index.php/jair/article/view/13428) 480 | ##### Distributions 481 | - Leemis, Lawrence M., and Jacquelyn T. McQueston. "Univariate distribution relationships." The American Statistician 62.1 (2008): 45-53. [[Paper]](https://www.math.wm.edu/~leemis/2008amstat.pdf) [[Website]](https://www.math.wm.edu/~leemis/chart/UDR/UDR.html). 482 | - Olszewski, Adrian. Challenging the cult of the prevalent normal distribution in nature. 2KMM, 2023. [[Link]](https://www.2kmm.pl/blog/On-the-ubiquity-of-skewness-in-nature/) 483 | ##### Statistical hypothesis testing (NHST) 484 | - Gelman, Andrew. “Commentary: P Values and Statistical Practice.” Epidemiology, vol. 24, no. 1, 2013, pp. 69–72. JSTOR. Accessed 10 Dec. 2023. [[Link]](https://stat.columbia.edu/~gelman/research/published/asa_pvalues.pdf) 485 | - Greenland, Sander et al. “Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.” European journal of epidemiology vol. 31,4 (2016): 337-50. doi:10.1007/s10654-016-0149-3 [[Link]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4877414/) 486 | - Lakens, Daniël. “Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses.” Social psychological and personality science vol. 8,4 (2017): 355-362. doi:10.1177/1948550617697177 [[Link]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5502906/) 487 | - Lin, Mingfeng, et al. “Research Commentary: Too Big to Fail: Large Samples and the p-Value Problem.” Information Systems Research, vol. 24, no. 4, 2013, pp. 906–17. JSTOR. Accessed 10 Dec. 2023. [[Link]](http://www.stat.ntu.edu.tw/download/%E6%95%99%E5%AD%B8%E6%96%87%E4%BB%B6/bigdata/Research%20Commentary%20-%20Too%20Big%20to%20Fail%20Large%20Samples%20and%20the%20p-Value%20Problem.pdf) 488 | - Lumley, Thomas et al. “The importance of the normality assumption in large public health data sets.” Annual review of public health vol. 23 (2002): 151-69. doi:10.1146/annurev.publhealth.23.100901.140546 [[Link]](https://pubmed.ncbi.nlm.nih.gov/11910059/) 489 | - Mohd Razali, Nornadiah, and Bee Yap. ‘Power Comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling Tests’. J. Stat. Model. Analytics, vol. 2, 01 2011. [[Link]](https://www.researchgate.net/publication/267205556_Power_Comparisons_of_Shapiro-Wilk_Kolmogorov-Smirnov_Lilliefors_and_Anderson-Darling_Tests) 490 | - Morey, Richard D et al. “The fallacy of placing confidence in confidence intervals.” Psychonomic bulletin & review vol. 23,1 (2016): 103-23. doi:10.3758/s13423-015-0947-8 [[Link]](https://pubmed.ncbi.nlm.nih.gov/26450628/) 491 | - Olzsewski, Adrian. Mann-Whitney (Wilcoxon) and Kruskal-Wallis FAIL to compare medians in general. Quantile regression should be used to compare medians instead. [[Link]](https://gist.github.com/adrianolszewski/2cec75678e1183e4703589bfd22fa8b2) 492 | - Olszewski, Adrian. On the p-values - links library significance ditching. Adrian Olszewski, 2022. [[Link]](https://docs.google.com/document/d/16KXpFW_nvF9l0eNlc56c-yY-B9VziPGWEkyDjibvwp8/edit) 493 | - Olzsewski, Adrian. Testing hypotheses through statistical models opens a universe of new possibilities. Learn how to improve your daily work with this approach. [[Link]](https://github.com/adrianolszewski/model-based-testing-hypotheses)Pernet, Cyril. “Null hypothesis significance testing: a short tutorial.” F1000Research vol. 4 621. 25 Aug. 2015, doi:10.12688/f1000research.6963.3 [[Link]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635437/) 494 | - Serdar, Ceyhan Ceran et al. “Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies.” Biochemia medica vol. 31,1 (2021): 010502. doi:10.11613/BM.2021.010502 [[Link]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745163/) 495 | - The American Statistician, Volume 73, Issue sup1 (2019) [[Link]](https://www.tandfonline.com/toc/utas20/73/sup1) 496 | - Verhagen, Arianne P., et al. ‘Is the p Value Really so Significant?*’. Australian Journal of Physiotherapy, vol. 50, no. 4, 2004, pp. 261–262. [[Link]](https://www.sciencedirect.com/science/article/pii/S0004951414601227) 497 | ### Posts and threads 498 | #### Bayesian Statistics 499 | - Camara-Escudero, Mauro. Variational Auto-Encoders and the Expectation-Maximization Algorithm. Mauro Camara-Escudero, 2020. [[Link]](https://maurocamaraescudero.netlify.app/post/variational-auto-encoders-and-the-expectation-maximization-algorithm/) 500 | - Patacchiola, Massimiliano. Evidence, KL-divergence, and ELBO. Massimiliano Patacchiola, 2021. [[Link]](https://mpatacchiola.github.io/blog/2021/01/25/intro-variational-inference.html) 501 | - Yao, Yuling. Bayes is guaranteed to overfit, for any model, any prior, and every data point. Yuling Yao, 2023. [[Link]](https://www.yulingyao.com/blog/2023/overfit/) 502 | #### General topics 503 | - Harrell, Frank. Classification vs. Prediction. Statistical Thinking, 2017. [[Link]](https://www.fharrell.com/post/classification/) 504 | #### Variable selection / Feature selection 505 | - gung Reinstate Monica (https://stats.stackexchange.com/users/7290/gung-reinstate monica). Algorithms for Automatic Model Selection. Cross Validated, https://stats.stackexchange.com/q/20856. [[Link]](https://stats.stackexchange.com/a/20856) 506 | - Shtoff, Alex. “Are polynomial features the root of all evil?". Alex Shtoff, 2024. [[Link]](https://alexshtf.github.io/2024/01/21/Bernstein.html) 507 | - Sribney, Bill. What are some of the problems with stepwise regression? StataCorp, 1996. [[Link]](https://www.stata.com/support/faqs/statistics/stepwise-regression-problems/) 508 | ### Talks, conferences, and videos 509 | #### Bayesian Statistics 510 | - Chopin, Nicolas, et al. "Bayesian Causal Inference for Real World Interactive Systems." Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021. [[Link]](https://bcirwis2021.github.io/schedule.html) 511 | - Jordan, Michael. Nonparametric Bayesian Methods: Models, Algorithms, and Applications II. UC Berkeley, 2017 [[Link]](https://youtu.be/yfLoxwjCGNY?si=wdmK-yGImMiPihDk) 512 | - Maxim Kochurov. State of Bayes Lecture Series. PyMC Labs, 2023. [[Link]](https://www.youtube.com/watch?v=X4y2UfU-2cs&list=PL1iMFW7frOOsh5KOcfvKWM12bjh8zs9BQ) 513 | - Pragmatic Data Scientists. Making Informed Decisions with Bayesianism: A Conversation with Kenneth, Statistician at Meta. Pragmatic Data Scientist, 2023. [[Link]](https://www.youtube.com/watch?v=6269mm4XQOI) 514 | #### Stochastic Processes 515 | - Hakenes, Hendrik. Ito's Lemma -- Some intuitive explanations on the solution of stochastic differential equations. University of Bonn, 2021. [[Link]](https://youtu.be/_4zyAFpJ_Z4?si=hd4aVpI3ClKavcyu) 516 | 517 | ## 📄 Text Mining and Natural Language Processing 518 | ### Books 519 | - Silge, Julia, and David Robinson. Text mining with R: A tidy approach. " O'Reilly Media, Inc.", 2017. [[Link]](https://www.tidytextmining.com/) 520 | ### Courses and lecture notes, posts 521 | ### Datasets 522 | - Horwood, Ghraham V. Humanitarian Assistance and Disaster Relief (HA/DR) Articles and Lexicon. V1, Harvard Dataverse, 2017, doi:10.7910/DVN/TGOPRU. [[Link]](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TGOPRU) 523 | ### Packages 524 | ### Papers 525 | - Goldberg, Yoav. "A primer on neural network models for natural language processing." Journal of Artificial Intelligence Research 57 (2016): 345-420. [[Link]](https://arxiv.org/abs/1510.00726) 526 | - Minaee, Shervin, et al. "Large Language Models: A Survey." arXiv preprint arXiv:2402.06196 (2024). [[Link]](https://arxiv.org/abs/2402.06196) 527 | ### Posts and threads 528 | ### Talks, conferences, and videos 529 | -------------------------------------------------------------------------------- /UNDER-REVIEW.md: -------------------------------------------------------------------------------- 1 | # Under review 2 | ## Disclaimer 3 | The following resources are being reviewed, there is no warranty about their quality nor do we consider them worth mentioning on the list. 4 | 5 | ## Resource list 6 | - Amat Rodrigo, Joaquin, and Javier Escobar Ortiz. Skforecast. 0.11.0, 2023, doi:10.5281/zenodo.8382788. [[Link]](https://github.com/JoaquinAmatRodrigo/skforecast) 7 | - ArcticDB. ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem. [[Link]](https://github.com/man-group/ArcticDB) 8 | - Awesome Production Machine Learning. A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning. [[Link]](https://github.com/EthicalML/awesome-production-machine-learning) 9 | - Barandas, Marília, et al. "TSFEL: Time series feature extraction library." SoftwareX 11 (2020): 100456. [[Paper]](https://www.softxjournal.com/article/S2352-7110(20)30001-7/fulltext) [[Code]](https://github.com/fraunhoferportugal/tsfel) 10 | - Bendersky, Eli. Variance of the sum of independent random variables. Eli Bendersky's website, 2009. [[Link]](https://eli.thegreenplace.net/2009/01/07/variance-of-the-sum-of-independent-variables) 11 | - Bonabeau, Eric. "Agent-based modeling: Methods and techniques for simulating human systems." Proceedings of the national academy of sciences 99.suppl_3 (2002): 7280-7287. [[Link]](https://www.pnas.org/doi/10.1073/pnas.082080899) 12 | - Ceja, Enrique Garcia. Behavior analysis with machine learning using R. Chapman and Hall/CRC, 2021. 13 | - Chernozhukov, V. & Hansen, C. & Kallus, N. & Spindler, M. & Syrgkanis, V. 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Proceedings of the National Academy of Sciences 109.22 (2012): 8388-8393. [[Link]](https://www.pnas.org/doi/full/10.1073/pnas.1205013109) 21 | - Fugue. A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites. [[Link]](https://github.com/fugue-project/fugue) 22 | - Iurii D. Katser and Vyacheslav O. Kozitsin, “Skoltech Anomaly Benchmark (SKAB).” Kaggle, 2020, doi: 10.34740/KAGGLE/DSV/1693952. [[Link]](https://github.com/waico/SKAB) 23 | - Kristensen, Laura, and Anton Vorobets. "Portfolio Optimization and Parameter Uncertainty." Available at SSRN (2024). [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4709317) 24 | - Hasz, Brendan. Bayesian Gaussian Mixture Modeling with Stochastic Variational Inference. Brendan Hasz, 2019. [[Link]](https://brendanhasz.github.io/2019/06/12/tfp-gmm.html) 25 | - Hasz, Brendan. 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