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.
Link to the schedule from Winter 2020
Link to the schedule from Fall 2020
The list of announcements for each class are here: Announcements.
| Week | Date | Topic | Readings |
|---|---|---|---|
| 1 | January 12, 2021 | Introduction to the Course | Assignment #1 released with associated code simulate.py Thought questions #1: - read Chapters 1, 2 and 3 from the notes.pdf |
| 1 | January 14, 2021 | Probability | |
| 2 | January 19, 2021 | Probability (cont.), and Announcements | |
| 2 | January 21, 2021 | Finish Probability (cont.), Start A First Step in Estimation: Sample Averages and Bias | |
| 3 | January 26, 2021 | Concentration Inequalities and Confidence | |
| 3 | January 28, 2021 | Sample Complexityand Bias-Variance , Start Formalizing Parameter Estimation | Thought questions #1 due Thought questions #2: - read Chapters 4, 5 and 6 from the notes.pdf |
| 4 | February 2, 2021 | Formalizing Parameter Estimation (cont.), some Intro to Opt. | |
| 4 | February 4, 2021 | MAP and MLE, and Bayesian estimation | Assignment #1 due on Friday Assignment #2 released with associated code |
| 5 | February 9, 2021 | Bayesian estimation (cont.) and conjugate priors | |
| 5 | February 11, 2021 | More example of posteriors, and MLE for univariate regression | |
| 6 | February 16, 2021 | No classes, Reading Week | |
| 6 | February 18, 2021 | No classes, Reading Week | |
| 7 | February 23, 2021 | Quiz Review Slides | |
| 7 | February 25, 2021 | In-class Quiz | Thought questions #2 due Thought questions #3: - read Chapters 7 and 8 from the notes.pdf |
| 8 | March 2, 2021 | (Multivariate) Gradient Descent | |
| 8 | March 4, 2021 | Introduction to Prediction and Optimal Predictors | |
| 9 | March 9, 2021 | Finish Optimal Predictors start Linear Regression and Optimization | Stepsize script used in class script_stepsizes.py |
| 9 | March 11, 2021 | Finish Linear Regression and Optimization | |
| 10 | March 16, 2021 | Polynomial Regression, and Generalization Error and Overfitting | Thought questions #3 due on Monday, March 15, at 11:59 pm Edmonton time Thought questions #4: - read Chapters 9, 10, and 11 from the notes.pdf |
| 10 | March 18, 2021 | Evaluation of Learned Models and Hypothesis Testing, start Regularization | Assignment #2 due on Friday Assignment #3 released with associated code |
| 11 | March 23, 2021 | Midterm Review | |
| 11 | March 25, 2021 | Midterm | |
| 12 | March 30, 2021 | Bias, variance and generalization error | |
| 12 | April 1, 2021 | Logistic regression and classification | Demo comparing Linear Regression and Logistic Regression |
| 13 | April 6, 2021 | Finish Logistic regression, start Bayesian linear regression | |
| 13 | April 8, 2021 | Bayesian predictors, and contrasting prediction intervals and confidence intervals | Thought questions #4 due |
| 14 | April 13, 2021 | Review class on whiteboard, with additional slides as a high-level overview: Final Review slides. | |
| 14 | April 15, 2021 | Cancelled, Practice Final session scheduled for April 22 or 26 | Assignment #3 due on Friday |
| Final | Wednesday, April 28, 2021, 9:00 a.m. | Final Exam | 2 hours. The exam is open-book. |