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 (even if only a little bit).
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.
| Week | Date | Topic | Readings |
|---|---|---|---|
| 1 | January 5, 2023 | Introduction to the Course, start Probability | Assignment #1 released with associated code and instructions Readings Exercises #1: - read Chapters 1, 2 and 3 from the notes.pdf |
| 2 | January 10, 2023 | Probability, start Multivariate Probability | |
| 2 | January 12, 2023 | Finish Multivariate Probability | |
| 3 | January 17, 2023 | (Move to Whiteboard) A First Step in Estimation: Sample Averages, Concentration Inequalities, Confidence and Sample Complexity | |
| 3 | January 19, 2023 | Bias and Variance, start Formalizing Parameter Estimation | Readings Exercises #1 due on Thursday, Jan. 19 Readings Exercises #2: - read Chapters 4, 5 and 6 from the notes.pdf |
| 4 | January 24, 2023 | Intro to Optimization | |
| 4 | January 26, 2023 | Finish optimization, start MLE | Assignment #1 due on Friday, Jan. 27 Assignment #2 released with associated code |
| 5 | January 31, 2023 | MLE and MAP | |
| 5 | February 2, 2023 | Bayesian estimation and conjugate priors | Readings Exercises #2 due Feb. 2 Readings Exercises #3: - read Chapters 7, 8 and 9 from the notes.pdf |
| 6 | February 7, 2023 | Summary parameter estimation, example showing need for gradient descent | |
| 6 | February 9, 2023 | Stochastic Gradient Descent and stepsize selection, Start Introduction to Prediction and Optimal Predictors (first half of slides slides) | Stepsize script used in class script_stepsizes.py |
| 7 | February 14, 2023 | Quiz Review Slides | |
| 7 | February 16, 2023 | In-class Quiz | |
| 8 | February 21, 2023 | No classes, Reading Week | Assignment #2 due on Tuesday, Feb. 21 Assignment #3 released with associated code. |
| 8 | February 23, 2023 | No classes, Reading Week | |
| 9 | February 28, 2023 | Finish Optimal Predictors on whiteboard | |
| 9 | March 2, 2023 | Linear Regression | Readings Exercises #3 due Thursday, March 2 Readings Exercises #4: - read Chapters 10, 11 and 12 from the notes.pdf |
| 10 | March 7, 2023 | Polynomial Regression, and Generalization Error and Overfitting | |
| 10 | March 9, 2023 | Evaluation of Learned Models and Hypothesis Testing | Assignment #3 due on Friday, March 10 Assignment #4 released with associated code. |
| 11 | March 14, 2023 | Midterm Review | |
| 11 | March 16, 2023 | Midterm | |
| 12 | March 21, 2023 | Regularization and bias and variance | |
| 12 | March 23, 2023 | Logistic regression, and polynomial logistic regression and adding regularization (l1 and l2) | Readings Exercises #4 due Thursday, March 23 |
| 13 | March 28, 2023 | Bayesian linear regression and contrasting prediction intervals and confidence interval | |
| 13 | March 30, 2023 | Finish Bayesian linear regression | |
| 14 | April 4, 2023 | Course review. | |
| 14 | April 6, 2023 | Finish course review. Conclude with an ML case study. | Assignment #4 due on Friday, April 7. |
| 15 | April 11, 2023 | Brief overview of topics for final (see slides), Practice Final session and Q&A | |
| Final | Monday, April 17, 2023, 9:00 a.m. | Final Exam | 2 hours. |