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# JSC370 and JSC470: Data Science II and III
# Winter 2021

### 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)

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