Please note that the items on this page are subject to change.
| Date | Topic | Subtopic | Section | Readings | Beyond Section | HW Release | HW Deadline |
|---|---|---|---|---|---|---|---|
| Week 0 | |||||||
| Jan. 17 (Tu) | HW 0 released (pre-requisites) | ||||||
| Jan. 19 (Th) | |||||||
| Jan. 20 (Fr) | |||||||
| Week 1 | Math, Statistics, and Code Review | ||||||
| Jan. 24 (Tu) | Intro to CS 181 | ||||||
| Jan. 26 (Th) | Regression | Regression | HW 1 released (regression) | HW 0 due (note: free, no-questions-asked extension to Feb 2) | |||
| Jan. 27 (Fr) | AI and Broader Impact | ||||||
| Week 2 | Regression | ||||||
| Jan. 31 (Tu) | Probabilistic Regression | ||||||
| Feb. 2 (Th) | Confidence and Uncertainty | ||||||
| Feb. 3 (Fr) | Kernels | ||||||
| Week 3 | Classification | ||||||
| Feb. 7 (Tu) | Classification | Probabilistic Classification | |||||
| Feb. 9 (Th) | Non-Probabilistic Classification | HW 2 released (classification) | HW 1 due | ||||
| Feb. 10 (Fr) | OOD, Uncertainty, and Interpretability | ||||||
| Week 4 | Neural Network Architectures and Optimization | ||||||
| Feb. 14 (Tu) | Neural Networks (NN) | Intro to NN | |||||
| Feb. 16 (Th) | More on NN | ||||||
| Feb. 17 (Fr) | NN Interpretability | ||||||
| Week 5 | Conjugate Pairs in Modeling | ||||||
| Feb. 21 (Tu) | Bayesian Modeling | Intro to Bayesian Models | |||||
| Feb. 23 (Th) | More on Bayesian Models | HW 3 released (neural networks and Bayes) | HW 2 due | ||||
| Feb. 24 (Fr) | Deep Bayes | ||||||
| Week 6 | Midterm I Review | ||||||
| Feb. 28 (Tu) | Bayesian Inference | ||||||
| Mar. 2 (Th) | Practical | Practical I Content | Practical I released (modeling) | ||||
| Mar. 3 (Fr) | Advanced Sampling and Variational Inference | ||||||
| Week 7 | Case Studies in AI and Ethics | ||||||
| Mar. 7 (Tu) | Midterm I | ||||||
| Mar. 9 (Th) | Ethics | Embedded Ethics Lecture | |||||
| Mar. 10 (Fr) | |||||||
| Week 8 | |||||||
| Mar. 14 (Tu) | Spring Break! | ||||||
| Mar. 16 (Th) | Spring Break! | ||||||
| Mar. 17 (Fr) | Spring Break! | ||||||
| Week 9 | Dimensionality Reduction and PCA | ||||||
| Mar. 21 (Tu) | Dimensionality Reduction | Dimensionality Reduction | |||||
| Mar. 23 (Th) | Latent Variable Models | Latent Variable Models and Expectation Maximization (EM) | HW 4 released (latent variables and EM) | HW 3 and Practical I due | |||
| Mar. 24 (Fr) | Variational Autoencoders (VAE) | ||||||
| Week 10 | EM | ||||||
| Mar. 28 (Tu) | Clustering | Clustering | |||||
| Mar. 30 (Th) | Topic Models | Topic Models | |||||
| Mar. 31 (Fr) | Representation Learning | ||||||
| Week 11 | Time Series | ||||||
| Apr. 4 (Tu) | Graphical Models | Graphical Models and Hidden Markov Models (HMM) | |||||
| Apr. 6 (Th) | Inference of HMMs | HW 5 released (HMM) | HW 4 due | ||||
| Apr. 7 (Fr) | Nonlinear Dynamics | ||||||
| Week 12 | Defining Markov Decision Processes (MDPs) | ||||||
| Apr. 11 (Tu) | HMMs and Learning | ||||||
| Apr. 13 (Th) | Markov Decision Processes (MDPs) | MDPs | |||||
| Apr. 14 (Fr) | Reinforcement Learning vs. Bandits | ||||||
| Week 13 | Reinforcement Learning (RL) Algorithms | ||||||
| Apr. 18 (Tu) | Reinforcement Learning (RL) | Model-Based RL | |||||
| Apr. 20 (Th) | Model-Free RL | Practical II released (RL and social-technical systems) | HW 5 due | ||||
| Apr. 21 (Fr) | Open Problems in RL | ||||||
| Week 14 | Midterm II Review | ||||||
| Apr. 25 (Tu) | Practical II Content | ||||||
| Apr. 27 (Th) | Midterm II | ||||||
| Apr. 28 (Fr) | ML Research Beyond 181 | Practical II due end of Reading Period (May 3) |