Course guide
The current 25-meeting course spine
This course is organized around the published lecture decks. The short calculus bridge is a distinct meeting, which is why the current sequence has 25 meetings rather than the older 24- and 26-lecture plans still preserved in the repository history.
How the course is built
Each module answers one question and hands a working idea to the next:
| Module | Meetings | Core question |
|---|---|---|
| Foundations | 1–4 | Why these losses, and how do we differentiate a network? |
| Optimization & training | 5–7 | How do we train deep networks reliably and make them generalize? |
| Vision | 8–11 | How do locality, hierarchy, and dense prediction change the model? |
| Sequences & language | 12–15 | How do models represent and generate ordered data? |
| Transformers | 16–17 | How does attention become a scalable architecture? |
| Representation & multimodal learning | 18–19 | How can data supervise itself, and how do images meet text? |
| Generative modeling | 20–23 | How do latent-variable, adversarial, and diffusion models generate? |
| Systems & synthesis | 24–25 | How do we serve models efficiently, and what ideas survive the whole course? |
The complete titles and canonical PDFs are on Lecture decks.
What to do each week
- Deck first. It contains the conceptual thread, a worked mechanism, and two short MCQ checkpoints with immediate answer slides.
- Notebook second. Use the companion only where calculation or implementation makes the idea clearer.
- Assignment third. Assignments extend already-seen code and value an explanation of your own measurements over a leaderboard number.
Materials
- Lecture decks — the canonical source for the current sequence.
- Notebooks — runnable companions and downloadable
.ipynbfiles. - Assignments — programming work, policy, and submission expectations.
- Resources — a deliberately short list of books, videos, papers, and interactives.
- Logistics & grading ↗ — schedule, grading, deadlines, and FAQ for the August 2026 offering.
Course norms
Bring questions to the course Slack workspace. For programming work, use AI tools honestly and document meaningful assistance; the assessed work is your ability to explain your own plots, decisions, and failures. The detailed policy lives on the Assignments page.