| Lecture # |
Date |
Topic and Slides |
Subtopics |
Reading Assignment |
| 1 |
Jan 22 |
T1: Learning |
(T0: Course Introduction), What is Learning? |
Shalev-Shwartz (Chap 2) |
| 2 |
Jan 24 |
Empirical Risk Minimization, Linear Regression |
Bishop (Chap 4) |
| 3 |
Jan 27 |
(Recorded Lecture to be posted in Panapto folder, in-class activities) Linear Classification, Perceptron |
Bishop (Chap 6) |
| 4 |
Jan 29 |
(Recorded Lecture to be posted in Panapto folder, in-class activities) Need for Deep Learning, Multi-Layer Perceptrons |
Bishop (Chap 6) |
| 5 |
Jan 31 |
(Reserve lecture for in-class activities) |
|
| 6 |
Feb 3 |
(Recorded Lecture to be posted in Panapto folder, in-class activities) Activation Functions |
Bishop (Chap 6) |
| 7 |
Feb 5 |
Optimization, First-Order Algorithms |
Bishop (Chap 7) |
| 8 |
Feb 7 |
Momentum, Adaptive Rate |
Bishop (Chap 7) |
| 9 |
Feb 10 |
Data Parallelism |
Bishop (Chap 7) |
| 10 |
Feb 12 |
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| 11 |
Feb 14 |
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| 12 |
Feb 17 |
T2: CNNs |
|
|
| 13 |
Feb 19 |
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| 14 |
Feb 21 |
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| 15 |
Feb 24 |
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| 16 |
Feb 26 |
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| 17 |
Feb 28 |
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| 18 |
Mar 3 |
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| 19 |
Mar 5 |
T3: Transformers |
|
|
| 20 |
Mar 7 |
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| 21 |
Mar 10 |
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| 22 |
Mar 12 |
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| 23 |
Mar 14 |
Spring Recess (No Class) |
|
| 24 |
Mar 17 |
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| 25 |
Mar 19 |
Exam |
Topics: |
|
| 26 |
Mar 21 |
|
Spring Break (No Class) |
|
| 27 |
Mar 24 |
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| 28 |
Mar 26 |
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| 29 |
Mar 28 |
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| 30 |
Mar 31 |
T4: GNNs |
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| 31 |
Apr 2 |
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| 32 |
Apr 4 |
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| 33 |
Apr 7 |
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| 34 |
Apr 9 |
T5: Deep RL |
|
|
| 35 |
Apr 11 |
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| 36 |
Apr 14 |
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| 37 |
Apr 16 |
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| 38 |
Apr 18 |
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| 39 |
Apr 21 |
T6: Generative Models |
|
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| 40 |
Apr 23 |
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| 41 |
Apr 25 |
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| 42 |
Apr 28 |
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| 43 |
Apr 30 |
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| 44 |
May 2 |
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| 45 |
May 5 |
Project Presentations |
|
|
| 46 |
May 7 |
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| 47 |
May 9 |
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