Readings
Students are expected to read the corresponding sections about a class’s topic from notes before class as each class will discuss each topic in more detail and address questions about the material.
All readings are from the (in progress) machine learning notes. These are designed to be short, so that you can read every chapter. I recommend avoiding printing these notes, since later parts of the notes are likely to be modified.
Schedule
This schedule may have small changes throughout the semester. Most of the lectures will be whiteboard lectures. The links to pdf files will be dead links, until we get to that lecture. I will upload all slides right before lecture, so you can follow along during class. The whiteboard lecture notes will be uploaded to a shared Google drive of lecture notes)
| Week | Date | Topic | Readings |
|---|---|---|---|
| 1 | January 7, 2025 | Introduction to the Course with a brief refresher of the basics of ML taught in ML I | Assignment #1 released Readings Chapters 1-4 from the ml2-notes.pdf |
| 1 | January 9, 2025 | Probability Concepts (multivariate distributions, mixture models, KL divergences), Probability slides | |
| 2 | January 14, 2025 | Revisiting Linear Regression, with matrices, including bias and variance (move to whiteboard, lecture notes in this folder) | |
| 2 | January 16, 2025 | Optimization concepts (Hessians, stepsize algorithms, minibatch sizes, convergence rates) | January 17 the Add/Drop deadline (no fees for change) |
| 3 | January 21, 2025 | Optimization concepts continued | Readings Exercises #1 due on Monday, Jan. 20 Readings Exercises #2: - read Chapters 5-7 |
| 3 | January 23, 2025 | Revisiting Multinomial Logistic Regression | Assignment #1 due on Friday, Jan. 24 Assignment #2 released |
| 4 | January 28, 2025 | Constrained Optimization and First in-class quiz |
In-class Quiz 1 about Assignment 1 (must attend class) |
| 4 | January 30, 2025 | Cross Validation to estimate generalization error and do hyperparameter selection, slides | |
| 5 | February 4, 2025 | The Goal of Data representations and Separability slides and do Course Review slides | Readings Exercises #2 due Feb. 3 Readings Exercises #3: - read Chapters 8-10 |
| 5 | February 6, 2025 | Learning data representations: latent variable methods and (probabilistic) PCA | Assignment #2 due on Friday, Feb. 7 Assignment #3 released |
| 6 | February 11, 2025 | Learning data representations with neural networks and second in-class quiz |
In-class Quiz 2 about Assignment 2 |
| 6 | February 13, 2025 | Finish neural networks, autoencoders and the connection to PCA | |
| 7 | February 17-21, 2025 | No classes, Reading Week | |
| 8 | February 25, 2025 | More advanced topics for generalization | Readings Exercises #3 due Monday, Feb 24 Readings Exercises #4: - read Chapters 11-13 |
| 8 | February 27, 2025 | Generative models: mixture models and EM | |
| 9 | March 4, 2025 | Midterm Review | Midterm is about topics from Chapter 1-9 |
| 9 | March 6, 2025 | Midterm in-class for the whole 90 minutes | |
| 10 | March 11, 2025 | (Conditional) Variational auto-encoders and adding complexity with data representations | |
| 10 | March 13, 2025 | Evaluating Generative Models and Missing Data | Assignment #3 due on Friday, March 14 Assignment #4 released |
| 11 | March 18, 2025 | Uncertainty estimation: Bayesian regression, Gaussian processes and bootstrap ensembles and third in-class quiz |
Readings Exercises #4 due Monday, March 17 Readings Exercises #5: - read Chapters 14-16 |
| 11 | March 20, 2025 | Finish uncertainty estimation | |
| 12 | March 25, 2025 | Partial observability and temporal data | |
| 12 | March 27, 2025 | Recurrent Neural Networks and friends | |
| 13 | April 1, 2025 | Finish topics | |
| 13 | April 3, 2025 | Course review | Assignment #4 due on Friday, April 4 |
| 14 | April 8, 2025 | Finish course review and practice final and fourth In-class quiz 4 |
Readings Exercises #5 due Monday, April 7 |
| Final | TBA | Final Exam | 2 hours |