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| Week: date | Topic(s) | Course | Readings | Additional information | |
|---|---|---|---|---|---|
| 1: 1/13 | Intro to Applied Analytics -- ML pipeline | Slides | State of AI (optional) | ||
| 1: 1/15 | Review of R; manipulating data; using tidyverse | R review | None | Bring your laptop to class with R and Rstudio installed | |
| 2: 1/20 Monday: no class, 1/22 | Decision trees and logistic regression | Slides | ISL 4.3, 4.6, 8, 2.2 | PS1 due 1/24 | |
| 3: 1/27, 1/29 | Evaluating predictions; logistic regression | Slides | ISL 4.3, 4.6, 8, 2.2 | PS2 due 1/31 | |
| 4: 2/3, 2/5 | Coding week: DT, LR, CV | Slides | |||
| 5: 2/10, 2/12 | Evaluating predictions; discussion of readings | reading 1 (abstract only); reading 2 | PS 3 due 2/14 | ||
| 6: 2/17, 2/19 | Missing data; Bayesian networks | Murphy 8.6, ESL 9.6, Murphy 10, 11 | PS 4 due 2/21 | ||
| 7: 2/24, 2/26 | Bayesian networks; ensembles | Slides | ISL 8.2, 6.2 | PS 5 due 2/28 | |
| 8: 3/2, 3/4 | Ensembles, GLM variants | Slides | ISL 8.2, 6.2, 7.3, 7.7 | Introduce final project 3/2, request project partners by 3/6 | |
| -: 3/9, 3/11 | Spring break | ||||
| 9: 3/16, 3/18 | GLM variants, neural networks 1 | Slides | Murphy 28; Deep Learning Cheat Sheet; Deep Learning 2017 (slides) | ||
| 3/23, 3/25 | Neural networks 2: MLPs, autoencoders | Slides | PS 6 due 3/23, Proposal due 3/25 | ||
| 3/30, 4/1 | Neural networks 3: CNNs, fine-tuning, RNNs | Slides | Supplementary reading: Understanding LSTMs, From GAN to WGAN | PS 7 due 4/3 | |
| 4/6, 4/8 | Neural networks 4: GANs; language modeling | Slides | Latent Dirichlet allocation; word2vec | PS 8 due 4/10 | |
| 4/13, 4/15 | Language modeling; survival analysis | Slides | CASI 9 | PS 9 due 4/17 | |
| 4/20, 4/22 | Survival analysis, technical debt | CASI 9; On technical debt; Rules of ML | PS 10 due 4/29 | ||
| 4/27 | Last day of S20 classes: final project workday | ||||
| -: 5/4 | (Finals week) | Final project due 5/4 |
Note: bold indicates graded material