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).
Other resources you can consult
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Machine Learning.
- T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning.
- David Barber, Bayesian Reasoning and Machine Learning.
- C.M. Bishop, Pattern Recognition and Machine Learning.
- Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Mathematics for Machine Learning.
Resources for Julia
Schedule
This schedule is tentative, and is likely to change throughout the semester. Most of the lectures will be whiteboard lectures. Note that Assignments are released on eClass along with instructions. Chapters below refer to those in notes.pdf unless otherwise indicated. Links to in-class slides are made available below before class, and after class the annotated slides will be this Google drive link, where the slides are named with the lecture date.
| Week | Date | Topic | Readings/Notes | Exams/Assignment |
|---|---|---|---|---|
| 1 | 09-Jan | Introduction | Chapter 1 | |
| 1 | 11-Jan | Probability | Chapter 2 | Assignment 1a PDF, Latex, and code |
| 2 | 16-Jan | Multivariate probability | Chapter 2 | |
| 2 | 18-Jan | Finish Multivariate probability, start Estimation: | Chapter 3 | Assignment 1b PDF, and Latex |
| 3 | 23-Jan | Estimation: Sample Averages, Bias, and Concentration Inequalities, Sample Complexity and the Bias-Variance Tradeoff | Chapter 3 | |
| 3 | 25-Jan | Finish Estimation Introduction to Optimization | Chapter 4 | |
| 4 | 30-Jan | Optimization Formalizing Parameter Estimation (MLE, MAP) | Chapter 5 | |
| 4 | 01-Feb | Parameter Estimation (MLE, MAP) | Chapter 5 | Assignment 1a and 1b due Feb 2 11:59 pm. Assignment 2a PDF, Latex, and code |
| 5 | 06-Feb | Bayesian Estimation and conjugate priors | Chapter 5 | |
| 5 | 08-Feb | Finish parameter estimation, stochastic gradient descent (Class cancelled because of illness; recorded lecture on eClass) | Chapter 6 | |
| 6 | 13-Feb | Review for Midterm Exam 1 Going over sample questions | ||
| 6 | 15-Feb | Midterm Exam 1 (Formula Sheet Will be provided at exam.) | Assignment 2b PDF and Latex | |
| 7 | 20-Feb | Reading week - no classes | ||
| 7 | 22-Feb | Reading week - no classes | ||
| 8 | 27-Feb | Post-view of Midterm Exam 1; Prediction and Optimal Predictors | Chapter 7 | |
| 8 | 29-Feb | Optimal Predictors and Linear Regression | Chapter 7 | Assignment 2b due Mar 3 11:59 pm. Assignment 3a PDF and Latex |
| 9 | 05-Mar | Linear Regression & Polynomial Regression | Chapter 8 | |
| 9 | 07-Mar | Generalization Error and Evaluation of Learned models | Chapter 9 | Assignment 2a due Mar 8 11:59 pm. Assignment 3a due Mar 8 11:59 pm. Assignment 3b PDF, Latex, and code |
| 10 | 12-Mar | Evaluation of Learned models and Regularization | Chapters 9 | |
| 10 | 14-Mar | Regularization, Bias-Variance | Chapter 10 | Assignment 3b due Mar 15 11:59 pm. Assignment 4a PDF, Latex, and code |
| 11 | 19-Mar | Logistic regression | Chapter 11 | |
| 11 | 21-Mar | Review for Midterm Exam 2 (This exam covers up to and including Chapter 8 in the Notes and up to and including March 5 lecture) | ||
| 12 | 26-Mar | Midterm Exam 2 (This exam covers up to and including Chapter 8 in the Notes and up to and including March 5 lecture) | ||
| 12 | 28-Mar | Logistic Regression, Bayesian Linear Regression | Chapter 12 | Assignment 4b PDF and Latex |
| 13 | 02-Apr | Bayesian linear regression | Assignment 4a due Apr 2 11:59 pm | |
| 13 | 04-Apr | Recap last lecture, Course overview | Chapter 12 | |
| 14 | 09-Apr | Continuation of course overview; ML case study | Chapter 12 | Assignment 4b due Apr 10 11:59pm |
| 14 | 11-Apr | Review of topics for final exam, practice final exam session | ||
| 18-Apr | 9:00 a.m. Tentative date for final exam - date will be confirmed by the University. See the Fall 2023/Winter 2024 Final Exam Planner on the Registrar’s website. |