What you’ll learn
G1 Build a training system
Implement and debug a neural network: predictions, losses, gradients, and training.
G2 Understand design choices
Explain design choices, test their tradeoffs, and judge what experiments establish.
G3 Reason about AI in the real world
Evaluate AI safety, ethics, ownership, control, and governance in real-world use.
G4 Apply AI in professional practice
Build, use, and adapt AI for real needs; make and communicate sound deployment decisions.
Weekly schedule
| Week / dates | Monday quiz | Tuesday / Thursday lectures | Homework & Programming Assignments | Weekly learning goal | Project milestones (tentative) |
|---|---|---|---|---|---|
| 00Sep 24–25 | No Monday meeting. |
Thu · Sep 24
Syllabus and AI today
|
HW0 Ungraded Released Sep 24 |
— | — |
| 01Sep 28–Oct 2 |
Sep 28 · Foundations clinic Discussion of HW0 and PA0 topics. |
Tue · Sep 29
ML settings and learning objectives
Thu · Oct 1
Deep learning vs. machine learning |
HW1 Released Sep 28 |
Understand when each type of machine learning objective is appropriate. Construct deep neural networks and compute a forward pass through a network. |
— |
| 02Oct 5–9 |
Oct 5 · Quiz 1 Learning settings, objectives, and forward computation. |
Tue · Oct 6
Backpropagation
Thu · Oct 8
Optimization |
HW2 Released Oct 5 |
Compute gradients, understand gradient descent, and learn to set the learning rate. |
— |
| 03Oct 12–16 |
Oct 12 · Quiz 2 Backpropagation, curvature, and gradient descent. |
Tue · Oct 13
Training deep networks
Thu · Oct 15
Evaluation and benchmarking |
HW3
PA3
Released Oct 12 |
— | — |