Schedule
The lectures will cover the course notes, which were made specifically for this course. These are designed to be short, so that you can read every chapter. After each lecture a link to the lecture notes and video will be added to the Lectures table below. Minor modifications to the course notes will be made throughout the term to improve clarity and address any issues that arise.
Assignments (28% total, 4% each)
| Assignment | Release Date | Due Date |
|---|---|---|
| Assignment 1 | Sep 12, 2025, 11:59pm MDT | Sep 19, 2025, 11:59pm MDT |
| Assignment 2 | Sep 19, 2025, 11:59pm MDT | Sep 26, 2025, 11:59pm MDT |
| Assignment 3 | Sep 26, 2025, 11:59pm MDT | Oct 03, 2025, 11:59pm MDT |
| Assignment 4 | Oct 17, 2025, 11:59pm MDT | Oct 24, 2025, 11:59pm MDT |
| Assignment 5 | Oct 24, 2025, 11:59pm MDT | Oct 31, 2025, 11:59pm MDT |
| Assignment 6 | Oct 31, 2025, 11:59pm MDT | Nov 07, 2025, 11:59pm MDT |
| Assignment 7 | Nov 21, 2025, 11:59pm MDT | Nov 28, 2025, 11:59pm MDT |
| Assignment 8 | Nov 28, 2025, 11:59pm MDT | Dec 05, 2025, 11:59pm MDT |
Lectures
| Week | Date | Content (Chapters refer to the course notes) |
|---|---|---|
| 01 | Sep 02 | Lec 1: Chap 1 (Introducing machine learning) | Slides | Video |
| 01 | Sep 04 | Lec 2: Chap 2 (Math review) | Math review notes: Live, Final | Video |
| 02 | Sep 09 | Lec 3: Chap 2 (Math review cont.) | Video |
| 02 | Sep 11 | Lec 4: Chap 3 (Probability) | Probability notes: Live, Final | Video |
| 03 | Sep 16 | Lec 5: Chap 3 (Probability cont.) |
| 03 | Sep 18 | Lec 6: Chap 4 (Supervised learning) |
| 04 | Sep 23 | Lec 7: Chap 5 (Estimation) |
| 04 | Sep 25 | Lec 8: Chap 6 (Optimization I: Closed form solutions) |
| 05 | Sep 30 | National Day for Truth and Reconciliation - No Classes |
| 05 | Oct 02 | Lec 9: Midterm exam 1 review |
| 06 | Oct 07 | Midterm exam 1 (21%): In class (12:35pm - 1:45pm in CCIS 1-440) |
| 06 | Oct 09 | Lec 10: Chap 6 (Optimization I: Closed form solutions cont.) |
| 07 | Oct 14 | Lec 11: Chap 6 (Optimization II: Gradient descent) |
| 07 | Oct 16 | Lec 12: Chap 6 (Optimization II: Gradient descent cont.) |
| 08 | Oct 21 | Lec 13: Chap 7 (Evaluating models) |
| 08 | Oct 23 | Lec 14: Chap 7 (Evaluating models cont.) |
| 09 | Oct 28 | Lec 15: Chap 8 (Evaluating models cont. & MLE) |
| 09 | Oct 30 | Lec 16: Chap 8 (MLE cont.) |
| 10 | Nov 04 | Lec 17: Chap 8 (MLE cont. & MAP) |
| 10 | Nov 06 | Lec 18: Midterm exam 2 review |
| 11 | Nov 11 | READING WEEK - No Classes |
| 11 | Nov 13 | READING WEEK - No Classes |
| 12 | Nov 18 | Midterm exam 2 (21%): In class (12:35pm - 1:45pm in CCIS 1-440) |
| 12 | Nov 20 | Lec 19: Chap 9 (Binary Classification) |
| 13 | Nov 25 | Lec 20: Chap 9 (Multiclass Classification) |
| 13 | Nov 27 | Lec 21: Chap 10 (Neural Networks) |
| 14 | Dec 02 | Lec 22: Chap 11 (Language Models) |
| 14 | Dec 04 | Lec 23: Final exam review |
| Dec 18 | Final Exam (30%): 3 hours, starts at 8:30am (tentative) |
Schedules from Past Years
Winter 2025, Fall 2024, Winter 2024, Winter 2023, Winter 2022, Fall 2021, Winter 2021, Fall 2020, Winter 2020.
Other Resources
- Khan Academy For math, probability, and linear algebra review
- Princeton Intro to ML Course Notes (COS 324)
Textbooks
- Deep Learning Foundations and Concepts (Christopher Bishop)
- Mathematics for Machine Learning (Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong)
- Understanding Machine Learning (Shai Shalev-Shwartz, Shai Ben-David) This is a more theory heavy book