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 is tentative, and is likely to change throughout the semester. Most of the lectures will be whiteboard lectures.
| Week | Date | Topic | Readings |
|---|---|---|---|
| 1 | January 6, 2021 | Introduction to the Course, start Probability | Assignment #1 released with associated code and instructions Thought questions #1: - read Chapters 1, 2 and 3 from the notes.pdf |
| 2 | January 11, 2021 | Probability, start Multivariate Probability | |
| 2 | January 13, 2021 | Finish Multivariate Probability | |
| 3 | January 18, 2021 | (Move to Whiteboard) A First Step in Estimation: Sample Averages, Concentration Inequalities, Confidence and Sample Complexity | |
| 3 | January 20, 2021 | Bias and Variance, start Formalizing Parameter Estimation | Thought questions #1 due on Thursday, Jan. 20 Thought questions #2: - read Chapters 4, 5 and 6 from the notes.pdf |
| 4 | January 25, 2021 | Intro to Optimization | |
| 4 | January 27, 2021 | MAP and MLE, and Bayesian estimation | Assignment #1 due on Friday, Jan. 28 Assignment #2 released with associated code |
| 5 | February 1, 2021 | Bayesian estimation (cont.) and conjugate priors | |
| 5 | February 3, 2021 | More example of posteriors, and MLE for univariate regression | Thought questions #2 due Feb. 3 Thought questions #3: - read Chapters 7, 8 and 9 from the notes.pdf |
| 6 | February 8, 2021 | Stochastic Gradient Descent and more on stepsize selection | Stepsize script used in class script_stepsizes.py |
| 6 | February 10, 2021 | Introduction to Prediction and Optimal Predictors | |
| 7 | February 15, 2021 | Quiz Review Slides | |
| 7 | February 17, 2021 | In-class Quiz | |
| 8 | February 22, 2021 | No classes, Reading Week | Assignment #2 due on Tuesday, Feb. 22 Assignment #3 released with associated code. |
| 8 | February 24, 2021 | No classes, Reading Week | Thought questions #3 due on Feb. 24 Thought questions #4: - read Chapters 10, 11 and 12 from the notes.pdf |
| 9 | March 1, 2021 | Finish Optimal Predictors (here are some extra slides that might be useful, but we won’t go over them), start Linear Regression | |
| 9 | March 3, 2021 | Finish Linear Regression and Polynomial Regression | |
| 10 | March 8, 2021 | Generalization Error and Overfitting, Start Evaluation of Learned Models and Hypothesis Testing | |
| 10 | March 10, 2022 | Regularization and bias and variance | Assignment #3 due on Friday, March 11 Assignment #4 released with associated code. |
| 11 | March 15, 2022 | Midterm Review | |
| 11 | March 17, 2022 | Midterm | |
| 12 | March 22, 2022 | Bias, variance for non-realizable functions, start logistic regression for classification | Thought questions #4 due Tuesday, March 22 |
| 12 | March 24, 2022 | Finish Logistic regression, and polynomial logistic regression and adding regularization (l1 and l2) | Demo comparing Linear Regression and Logistic Regression |
| 13 | March 29, 2022 | Bayesian linear regression and contrasting prediction intervals and confidence interval | |
| 13 | March 31, 2022 | Finish Bayesian linear regression, then start Review class on whiteboard. | |
| 14 | April 5, 2022 | Final Review slides highlighting which topics are tested, with Q&A session. Conclude with a few ML case studies. | |
| 14 | April 7, 2022 | Class cancelled, Practice Final session scheduled for April 11 | Assignment #4 due on Friday, April 8. |
| Final | Wednesday, April 13, 2022, 9:00 a.m. | Final Exam | 2 hours. |