This is a tentative schedule only. As the course progresses, the schedule will be adjusted.
| Week | Module | Topic | Readings (textbooks) |
|---|---|---|---|
| Sept 7-11 | 2020: Logistics & Website | Welcome & Logistics (video) Goals (video) Resources (video) Schedule (video) Assignments (video) Kaggle Competition (video) Project (video) Marks (video) | |
| 2019: 1 | Introduction to Machine Learning (slides) (video) | ||
| 2019: 2 | K-nearest neighbours (slides) (video) | [RN] Sec. 18.8.1, [HTF] Sec. 2.3.2, [D] Chapt. 3, [B] Sec. 2.5.2, [M] Sec. 1.4.2 | |
| Sept 14-18 | 2019: 3 | Linear regression (slides) (Notation Reference Sheet) (video) | [RN] Sec. 18.6.1, [HTF] Sec. 2.3.1, [D] Sec. 7.6, [B] Sec. 3.1, [M] Sec. 1.4.5 |
| 2019: 4 | Statistical learning (slides) (video) | [RN] Sec. 20.1, 20.2, [M] Sec. 2.2, 3.2 | |
| Sept 21-25 | 2019: 5 | Linear regression by maximum likelihood, maximum a posteriori, Bayesian learning (slides) (video) | [B] Sec. 3.1-3.3, [M] Chap. 7 |
| 2019: 7 | Mixture of Gaussians (slides) (video) | [B] Sec. 4.2, [M] Sec. 4.2 | |
| Sept 29 | Assignment 1 due (11:59 pm) | ||
| Sept 28 - Oct 2 | 2019: 8 | Logistic regression, generalized linear models (slides) (video) | [RN] Sec. 18.6.4, [B] Sec. 4.3, [M] Chap. 8, [HTF] Sec. 4.4 |
| 2019: 9 | Perceptrons, single layer neural networks (slides)(video) | [D] Chapt. 4, [HTF] Chapt. 11, [B] Sec. 4.1.7, 5.1, [M] Sec. 8.5.4, [RN] Sec. 18.7 | |
| 2019: 10 | Multi-layer neural networks, backpropagation (slides) (video) | [D] Chapt. 10, [HTF] Chapt. 11, [B] Sec. 5.2, 5.3, [M] Sec. 16.5, [RN] Sec. 18.7 | |
| Oct 6 | Assignment 2 due (11:59 pm) | ||
| Oct 5-9 | 2019: 11 | Kernel methods (slides) (video) | [D] Chapt. 11, [B] Sec. 6.1, 6.2 [M] Sec. 14.1, 14.2 [HTF] Chap. 6 |
| 2019: 12 | Gaussian Processes (slides (slides 12 and 22 revised Oct 13)) (video) | [B] Sec. 6.4 [M] Chap. 15 [HTF] Sec. 8.3 | |
| 2019: 13 | Support vector machines (slides) (video) | [B] Sec. 7.1 [D] Sec. 11.5-11.6 [HTF] Chap. 12 [M] Sec. 14.5 [RN] Sec. 18.9 [MRT] Chap. 4 | |
| Oct 19 | Assignment 3 due (11:59 pm) | ||
| Oct 19-23 | 2019: 14 | Support vector machines continued (slides) (video) | [B] Sec. 7.1 [D] Sec. 6.7 [HTF] Chap. 12 [M] Sec. 14.5 [RN] Sec. 18.9 [MRT] Chap. 4 |
| 2019: 15 | Deep neural networks (slides) (video) | [GBC] Chap. 6, 7, 8 | |
| 2019: 16 | Convolutional neural networks (slides ) (video) | [GBC] Chap. 9 | |
| Oct 29 | Assignment 4 due (11:59 pm) | ||
| Oct 26-30 | 2019: 17 | Hidden Markov models (slides) (video) | [RN] Sec. 15.3 [B] Sec. 13.1-13.2 [M] Sec. 17.3-17.5 |
| 2019: 18 | Recurrent neural networks (slides) (video) | [GBC] Chap. 10 | |
| Nov 2 | CS680 Grad Project Proposal due (11:59 pm) | ||
| Nov 2-6 | 2019: 19 | Attention and transformer networks (slides) (video) | [Vaswani et al., Attention is All You Need, NeurIPS, 2017] |
| 2019: 20 | Autoencoders (slides) (video) | [GBC] Chap. 14 | |
| Nov 9 | Assignment 5 due (11:59 pm) | ||
| Nov 9-13 | 2019: 21 | Generative networks (variational autoencoders and generative adversarial networks) (slides) (video) | [GBC] Chap. 20 |
| 2019: 23 | Normalizing Flows (guest lecture by Priyank Jaini) (slides) (video) | ||
| Nov 16-20 | 2019: 22 | Ensemble learning: bagging and boosting (slides) (video) | [RN] Sec 18.10, [M] Sec. 16.2.5, [B] Chap. 14, [HTF] Chap. 15-16, [D] Chap. 11 |
| 2019: 24 | Gradient boosting, bagging, decision forests (slides) (video) | [RN] Sec 18.10, [M] Sec. 16.2.5, 16.4.5, [B] Chap. 14, [HTF] Chap. 10, 15-16, [D] Chap. 13 | |
| Nov 20 | Assignment 6 due (11:59 pm) | ||
| Dec 8 | Kaggle Competition due (11:59 pm) | ||
| Dec 18 | CS680 Grad Project Report due (11:59 pm) | ||