Machine Learning

CS760, Fall 2021
Department of Computer Sciences
University of Wisconsin–Madison

Important Notes

This schedule is tentative and subject to change. Please check back often. In particular, the deadlines for the homework sets/project may change; please see Canvas for the actual deadlines.

How to interpret the column Readings in the Tentative Schedule

The reading is not required but strongly recommended for all students. Those explicitly noted as optional are for students interested in that specific topic. "A, B; C; D" means to read (A OR B) AND C AND D. Text in red means a link to the reading material. Abbreviations for textbooks:

Tentative Schedule

Date Lecture Readings Homework Released Homework Due
Thursday Sept. 9 Course Overview: Topics, Goals, Evaluation [Slides] [Recording] Jordan and Mitchell, Science, 2015 Homework 1 (background test)
Tuesday Sept. 14 ML Overview: Supervised/Unsupervised/RL, Classification/Regression, General Approach [Slides] [Quiz] [Recording]
Thursday Sept. 16 Supervised Learning I: Setup, Examples. Instance-Based Learning, Decision Trees [Slides] [Quiz] [Recording] Homework 1 due
Tuesday Sept. 21 Supervised Learning II: Setup + Examples. Decision Trees [Slides] [Quiz] [Recording] Murphy Chapter 16.2 / Shalev-Shwartz and Ben-David Chapter 18
Thursday Sept. 23 Evaluation: Bias, Cross-Validation, Precision/Recall, ROC Curves [Slides] [Quiz] [Recording: Part I] [Recording: Part II] Homework 2 out
Tuesday Sept. 28 Regression I: Linear Regression, Logistic Regression, Normal equations, GD [Slides] [Quiz] [Recording] Murphy 8.1-3 and 8.6; Maximum Likelihood, Logistic Regression, and Stochastic Gradient Training
Thursday Sept. 30 Regression II: Logistic Regression, Gradient Descent Analysis, SGD [Slides] [Quiz] [Recording] Convergence Theorems for Gradient Descent Homework 2 due
Tuesday Oct. 5 Naive Bayes: Generative vs Discriminative Models, ML vs MAP [Slides] [Quiz] [Recording] Mitchell 6.1-6.10, Murphy 3 Homework 3 out
Thursday Oct. 7 Neural Networks I: Perceptron, Training, MLPs[Slides] [Quiz] [Recording] Mitchell chapter 4, Murphy 16.5 and 28, Bishop 5.1-5.3 LeCun et al., Nature, 2015
Tuesday Oct. 12 Neural Networks II: Training, Optimization, SGD, Backpropagation[Slides] [Quiz] [Recording] Homework 3 due
Thursday Oct. 14 Neural Networks III: Regularization, Data Augmentation [Slides] [Quiz] [Recording] Homework 4 out
Tuesday Oct. 19 Neural Networks IV: CNNs [Slides] [Quiz] [Recording] Goodfellow-Bengio-Courville chapter 9; CNN papers 1. LeNet 2. AlexNet 3. ResNet
Thursday Oct. 21 Neural Networks IV: RNNs [Slides] [Quiz] [Recording] Goodfellow-Bengio-Courville chapter 10; Optional papers to read for part 4: 1. LSTM 2. GRU Homework 4 due (pushed to Monday)
Tuesday Oct. 26 Practical Aspects of Training + Review [Slides] [Recording]
Wednesday Oct. 27 Midterm
Thursday Oct. 28 Generative Models: Autoregressive, Flows, GANs [Slides] [Recording] Optional tutorial: Goodfellow's GAN tutorial; Optional papers: 1. Normalizing Flows for Probabilistic Modeling and Inference 2. Generative Adversarial Networks (GANs)
Tuesday Nov. 2 Kernels + SVMs:Margins, Support Vectors, Kernels [Slides] [Recording] [Quiz] Andrew Ng's note on SVM, Ben-Hur and Weston's note; Mohri-Rostamizadeh-Talwalkar Appendix B (Convex Optimization), Bishop Appendix E (Lagrange Multipliers) Homework 5 out
Thursday Nov. 4 Graphical Models I: Bayesian Networks, Training, Structure Learning [Slides] [Recording] [Quiz]
Tuesday Nov. 9 Graphical Models II: Undirected Models, Markov Random Fields [Slides] [Recording] [Quiz] Mitchell chapter 6, Bishop chapter 8.1, Shalev-Shwartz and Ben-David chapter 24; Heckerman Tutorial, Wainwright and Jordan Chapters 2, 3 Homework 6 out Homework 5 due
Thursday Nov. 11 Less-than-full Supervision: Semi-Supervised Learning, Weak Supervision, Self-Supervision [Slides] [Recording] [Quiz] Weak Supervision Notes
Tuesday Nov. 16 Unsupervised Learning I: Clustering, GMM models, EM [Slides] [Quiz] Homework 7 out
Thursday Nov. 18 Unsupervised Learning II: Dimensionality Reduction, PCA [Slides] [Recording] [Quiz] Homework 6 due
Tuesday Nov. 23 Learning Theory: Generalization, PAC [Slides] [Recording] Mohri-Rostamizadeh-Talwalkar Chapter 2, Mitchell Chapter 7 Homework 7 due
Tuesday Nov. 30 Reinforcement Learning I: MDPs, Value Iteration, Policy Iteration [Slides] [Recording] [Quiz] Mitchell Chapter 13
Thursday Dec. 2 Reinforcement Learning II: Q-learning, Approximation [Slides] [Recording] [Quiz] Homework 8 Out
Tuesday Dec. 7 Reinforcement Learning III: Function Approximation, Policy Search, Reinforce [Slides] [Recording]
Thursday Dec. 9 Large Language Models: Word Embeddings, Transformers, Pretraining [Slides] [Recording] On the Opportunities and Risks of Foundation Models
Tuesday Dec. 14 Fairness & Ethics [Slides] [Recording] Homework 8, Project due
Monday Dec. 20 (12:25PM - 2:25PM) Final Exam