Schedule - CMSC422 Spring 2026
This schedule is tentative and subject to change. Readings are from the textbook "A Course in Machine Learning" and supplementary materials. Key deadlines are included below.
| Week | Topics & Readings | Assignments & Exams |
|---|---|---|
| 1 (Feb 3, Feb 5) |
Topic: Course Introduction, Review of Probability and Linear Algebra Reading: Probability review, Linear algebra review Slides: lecture_01-02-03-2026.pdf, lecture_02-02-05-2026.pdf |
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| 2 (Feb 10, Feb 12) |
Topic: Introduction to learning, Decision Trees Reading: Chapter 1 and 2 of textbook Slides: lecture_03-02-10-2026.pdf, lecture_04-02-12-2026.pdf |
- |
| 3 (Feb 17, Feb 19) |
Topic: Decision Trees (cont.), Ensemble Learning, K-Nearest Neighbors Reading: Chapter 3 of textbook Slides: lecture_05-02-17-2026.pdf, lecture_06-02-19-2026.pdf |
Project 1 assigned |
| 4 (Feb 23, Feb 25) |
Topic: K-Means Clustering, Perceptron Reading: Chapter 4 of textbook Slides: lecture_07-02-24-2026.pdf, lecture_08-02-26-2026.pdf |
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| 5 (Mar 3, Mar 5) |
Topic: Perceptron, Practal Issues Reading: Chapter 4, Chapter 5 of textbook Slides: lecture_09-03-03-2026.pdf, lecture_10-03-05-2026.pdf |
Project 1 due Mar 10 |
| 6 (Mar 10, Mar 12) |
Topic: Midterm Review, Midterm Reading: Practice Exam Slides: lecture_11-03-10-2026.pdf |
|
| 7 (Mar 17, Mar 19) |
Topic: Spring Break - No Classes |
- |
| 8 (Mar 24, Mar 26) |
Topic: Reductions, Bias and Fairness Reading: Chapter 8 of textbook Slides: lecture_12-03-24-2026.pdf, lecture_13-03-26-2026.pdf |
Project 2 assigned |
| 9 (Mar 31, Apr 2) |
Topic: Binary Classification, Gradient Descent Reading: Chapter 7 of textbook Slides: lecture_14-04-02-2026.pdf, lecture_15-04-07-2026.pdf |
|
| 10 (Apr 7, Apr 9) |
Topic: Probabilistic View of ML, PCA Reading: Chapter 9, 15 of textbook Slides: lecture_16-04-07-2026.pdf, lecture_17-04-09-2026.pdf |
Midterm review |
| 11 (Apr 14, Apr 16) |
Topic: Neural Networks Reading: Chapter 10 of textbook Slides: lecture_18-04-14-2026.pdf, lecture_19-04-16-2026.pdf |
- |
| 12 (Apr 21, Apr 23) |
Topic: Deep Learning I Reading: Chapter 11 of textbook Slides: lecture_20-04-21-2026.pdf |
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| 13 (Apr 28, Apr 30) |
Topic: Deep Learning II, Kernel Methods Reading: Chapter 11 of textbook Slides: lecture_21-04-28-2026---iclr-travel.pdf, lecture_22-04-30-2026.pdf |
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| 14 (May 5, May 7) |
Topic: Kernels & Support Vector Machines (SVM), Intro to Autoencoders Reading: Chapter 11 of textbook Slides: lecture_23-05-05-2026.pdf, lecture_24-05-07-2026.pdf |
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