Fall 2026 · Tentative schedule
Lecture index
Topics may shift as the semester develops. Open a lecture to find its notes, slides, examples, code, and references.
WhenMon & Wed
12:30–1:45 PM
12:30–1:45 PM
WhereTSRB 132
InstructorKai Wang
Drop-in hoursMonday, 3:00–4:00 PM · KACB 3121
Lecture 1
Introduction
Lectures 2–5
Unsupervised learning
Discover structure in data through clustering, dimensionality reduction, and density estimation.
Lectures 6–13
Supervised learning
Learn predictive models from labeled data, from decision trees to neural networks.
Lectures 14–25
Advanced topics
Explore generative models, sequence models, attention, and reinforcement learning.
16
Oct 26
Diffusion models
17
Oct 28
Diffusion models
18
Nov 4
RNN, LSTM
Materials
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19
Nov 9
Transformer
Materials
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20
Nov 11
21
Nov 16
22
Nov 18
Deep reinforcement learning
Materials
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23
Nov 23
Topic to be announced
Materials
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24
Nov 30
Topic to be announced
Materials
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25
Dec 2
Topic to be announced
Materials
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Blank schedule entries indicate topics to be announced. Assessment information is communicated separately.
Teaching team
Questions and drop-in hours
Shreeyash Gowaikar
Wednesday, 2:30–3:30 PM · KACB 2nd Floor
Soumil Sahu
Tuesday, 10:00–11:00 AM · KACB 1st Floor
Adrian Ho
Friday, 2:00–3:00 PM · CODA 2nd Floor