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jsc370.github.io
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# JSC370 and JSC470: Data Science II and III # Winter 2021 <img src="assets/datascience.png" width="400"> ### Where and When * Instructor: [David Duvenaud](http://www.cs.toronto.edu/~duvenaud) * Teaching Assistant: [harsh Panchal](https://www.linkedin.com/in/harsh-panchal-618260151) * Email: <duvenaud@cs.toronto.edu>, please put "JSC370" or "JSC470" in the title. * Location: Zoom (see Quercus for details) * Time: Tuesday and Thursdays, 3-5pm * Office hours: Wednesdays 4-5 by zoom. * Course Forum: [Discourse](https://bb-2021-01.teach.cs.toronto.edu/c/jsc370) * [Course syllabus](syllabus.pdf) ## Course Structure ## Tentative Schedule --- ### Week 1: Background, motivation, course setup January 12 Lecture: [Video](https://play.library.utoronto.ca/9ac9b25727a49764c13ad038400c32f8) | [Slides](lectures/lec1.pdf) - [We don't need data scientists, we need data engineers](https://news.ycombinator.com/item?id=25775872) - [Data Science Subreddit](https://www.reddit.com/r/datascience/) - Has great discussion of what jobs are available, career trajectories and considerations, common problems, etc. January 12 Tutorial: Review of Python, Numpy, Pandas, Git, Colab [Video](https://play.library.utoronto.ca/ff44be69e8e5f022da9f587e1ac457b5) *** ### Week 2 January 19: Guest Lecture: [Ben Allison, Principal Machine Learning Scientist at Amazon](https://www.linkedin.com/in/ben-allison-2b881458/?originalSubdomain=uk) | [Video](https://play.library.utoronto.ca/e8aa287ea9fe2fbb90c6489077c4588f) January 21: Lecture on Latent variable models and collaborative filtering, intro to Assignment 1 - [Slides](lectures/lec2.pdf) | [Video](https://play.library.utoronto.ca/6f3f406963e6cfd5c8f2b60b620385b5) - [Intro to JAX](https://colinraffel.com/blog/you-don-t-know-jax.html) - [Collaborative Filtering and the Missing at Random Assumption](https://arxiv.org/abs/1206.5267) - [If It’s Worth Doing, It’s Worth Doing With Made-Up Statistics](https://slatestarcodex.com/2013/05/02/if-its-worth-doing-its-worth-doing-with-made-up-statistics/) - [Intro to probabilistic matrix factorization](https://towardsdatascience.com/probabilistic-matrix-factorization-b7852244a321) *** ### Week 3 January 25: Lecture 3: Confounding, censoring, and assignment 1 lab. [Video](https://play.library.utoronto.ca/85b2043bfbcd0ea1a69967c10ea4951c) | [Assigment 1](https://drive.google.com/file/d/1tyIy_zP8_rKoLBxFSIjgBw5wo2CTKrRo/view?usp=sharing) Some links on confounding and Simpson's paradox: - [Beware Regional Scatterplots](https://slatestarcodex.com/2016/04/02/beware-regional-scatterplots/) - [Beware Summary Statistics](https://slatestarcodex.com/2015/05/19/beware-summary-statistics/) - [Intro to Simpson's paradox](https://towardsdatascience.com/simpsons-paradox-how-to-prove-two-opposite-arguments-using-one-dataset-1c9c917f5ff9) January 21: Assignment 1 Presentations [Video](https://play.library.utoronto.ca/badf6257e5dee680e54d4a75db185bc8) *** ### Week 4 Feb 1st: Assignment 1 due by midnight. February 2nd: Guest Lecture: [Farah Bastien, Manager, Data Science/Data Engineer at MLSE (Maple Leaf Sports & Entertainment Partnership)](https://www.linkedin.com/in/farahbastien/?originalSubdomain=ca): Sports Analytics for the Leafs and the Raptors. [Video](https://play.library.utoronto.ca/f528b2c0772e5ed05dee6ad27a25126f) February 4th: Shapley values, causality, and Pearl's do-calculus. [Video](https://play.library.utoronto.ca/eec9e86d463adec4e535687f9a512e32) - [Slides introducing Do-calculus](https://www.cs.ubc.ca/labs/lci/mlrg/slides/doCalc.pdf) - [SHAP values explained exactly how you wished someone explained to you](https://towardsdatascience.com/shap-explained-the-way-i-wish-someone-explained-it-to-me-ab81cc69ef30) - [A Unified Approach to Interpreting Model Predictions](https://arxiv.org/abs/1705.07874) - [Making sense of Shapley values](https://towardsdatascience.com/making-sense-of-shapley-values-dc67a8e4c5e8) - [Causal Shapley Values](https://arxiv.org/abs/2011.01625) - [Problems with Shapley-value-based explanations as feature importance measures](http://proceedings.mlr.press/v119/kumar20e/kumar20e.pdf) *** ### Week 5 February 9: Assignment 2 lab [Video](https://play.library.utoronto.ca/cf85f578b8a08921a1e38d70bf9a76e3) February 11: Assignment 2 presentations [Video](https://play.library.utoronto.ca/0814e9dea30a4b6e375ea17c7f39931e) *** ### Week 6 Feb 17: Assignment 2 due by midnight. Reading Week *** ### Week 7 February 23: Guest Lecture: Wanying Zhao, Study design at Trilliam Foundation [Video](https://play.library.utoronto.ca/8173bdfd15bef2dcb680b7c6973b7d50) February 25: Natural Language processing [Video](https://play.library.utoronto.ca/578a8dc55e4364f50c35e8547fee787d) - [Latent Semantic Aalysis](https://en.wikipedia.org/wiki/Latent_semantic_analysis) - [Topic Modeling + LDA Slides](https://www.cs.cmu.edu/~mgormley/courses/10701-f16/slides/lecture20-topic-models.pdf) - [Original LDA Paper](https://jmlr.org/papers/volume3/blei03a/blei03a.pdf) - [Illustrated Word2Vec](https://jalammar.github.io/illustrated-word2vec/) - [RNN + Deep Language Model Slides](http://cs231n.stanford.edu/slides/2018/cs231n_2018_lecture10.pdf) - [Talk to Transformer](https://app.inferkit.com/demo) - [To What Extent is GPT-3 Capable of Reasoning?](https://www.lesswrong.com/posts/L5JSMZQvkBAx9MD5A/to-what-extent-is-gpt-3-capable-of-reasoning) *** ### Week 8 March 2nd: Assignment 3 Lab [Video](https://play.library.utoronto.ca/bacc6a397cf2a28a24e42c7507e98f37) March 4th: Assignment 3 presentations [Video](https://play.library.utoronto.ca/1d11ee6c397775bdd7d694586ffdf24b) *** ### Week 9 March 9: Guest Lecture: [Alp Kucukelbir, Chief Scientist, Fero Labs](https://www.proditus.com/) [Video](https://play.library.utoronto.ca/22cba9ced54676d7dc8c1343db51e574) March 10th: Assignment 3 due by midnight. March 11: Lecture: Time Series [Video](https://play.library.utoronto.ca/c346a6972624404d689c715770f7dc87) *** ### Week 10 March 16: Assignment 4 Lab [Video](https://play.library.utoronto.ca/b701c477d58e3e6608e1ad3fd43b97a9) March 18: Assignment 4 presentations *** ### Week 11 March 22: Assignment 4 due by midnight. March 23: Guest Lecture: Robert Grant, Cancer Genomics [Video](https://play.library.utoronto.ca/f0238765cf9d33b07a40de5a6eb6b94f) March 25: Surival analysis and clustering [Video](https://play.library.utoronto.ca/062f8dc84c68a7dba4e4143ee27da0d7) - [Nathan's lecture slides](2020/jsc370_classforA2ver1.1.pdf) - [Proportional Hazards Lecture Notes](https://www.math.ucsd.edu/~rxu/math284/slect5.pdf) - [Gaussian Mixture Model Slides](https://www.cs.cmu.edu/~epxing/Class/10708-15/slides/lecture8-EM.pdf) Related reading: - [Related blog post](https://towardsdatascience.com/deep-learning-for-survival-analysis-fdd1505293c9) - [DeepSurv paper](https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-018-0482-1) - [Cox-nnet](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006076) - [RNN-Surv](http://medianetlab.ee.ucla.edu/papers/RNN_SURV.pdf) - [Time-to-Event Prediction with Neural Networks and Cox Regression](https://jmlr.org/papers/volume20/18-424/18-424.pdf) *** ### Week 12 March 30: Lecture 11: Assignmnent 5 lab [Video](https://play.library.utoronto.ca/1853bf647eed425d83612c5c56d09398) April 1: Assignment 5 presentations *** ### Week 13 April 6: Lecture 12: Stochastic Variational Inference for capturing uncertainty [Video](https://play.library.utoronto.ca/015a6b300766a7b4478eb5983cdbc099) - [Bayesian NNs Colab 1](https://colab.research.google.com/github/papercup-open-source/tutorials/blob/master/intro_bnn/Bayesian_neural_networks_Part_1.ipynb) - [Bayesian NNs Colab 1](https://colab.research.google.com/github/papercup-open-source/tutorials/blob/master/intro_bnn/Bayesian_neural_networks_Part_2.ipynb) April 8: Short paper presentations April 12: Assignment 5 due by midnight. *** Extra reading: - [Replacing statistics with modern predictive models](https://cerebralab.com/Replacing_statistics_with_modern_predictive_models) - [Blog post](https://towardsdatascience.com/unintended-consequences-and-goodharts-law-68d60a94705c) - [Machine Learning: The High Interest Credit Card of Technical Debt](https://research.google/pubs/pub43146/)
