Schedule
Date | Lecture | Readings | ||
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08/19 |
Lecture #1
(Bo Dai):
Course Overview [ slides ] |
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Module I: Background Knowledge | ||||
08/21 |
Lecture #2
(Bo Dai):
Optimization: convex preliminary [ notes ] |
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08/26 |
Lecture #3
(Bo Dai):
Optimization: convex set and function I [ notes ] |
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08/28 |
Lecture #4
(Bo Dai):
Optimization: convex set and function II [ notes ] |
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09/02 | No Class (Labor Day) | |||
09/04 |
Lecture #5
(Bo Dai):
Optimization: gradient descent & Density Parametrization I [ notes ] |
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09/09 |
Lecture #6
(Bo Dai):
Density Parametrization II [ notes ] |
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09/09 | Team Formation Due | |||
09/11 |
Lecture #7
(Bo Dai):
Sampling: Basic Sampling, Acceptance-Rejection & Importance Sampling [ notes ] |
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09/16 |
Lecture #8
(Bo Dai):
Sampling: MCMC (MH, Gibbs & Hamiltonian) [ notes ] |
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09/18 |
Lecture #9
(Bo Dai):
Neural Network Revisit [ notes ] |
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Module II: Deep Generative Models | ||||
09/23 |
Lecture #10
(Bo Dai):
EBM (CD, Score Matching) [ notes ] |
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09/25 |
Lecture #11
(Bo Dai):
EBM and Diffusion [ notes ] |
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09/30 |
Lecture #12
(Bo Dai):
VAE and Diffusion I [ notes ] |
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10/02 |
Lecture #13
(Bo Dai):
VAE and Diffusion II [ notes ] |
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10/07 |
Lecture #14
(Bo Dai):
Autoregressive Model [ notes ] |
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10/09 |
Lecture #15
(Bo Dai):
Generative Adversarial Nets (GAN) and Normalizing Flow Models [ notes ] |
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10/14 | Fall Break | |||
Module III: Representation Learning | ||||
10/16 |
Lecture #16
( Runyu Zhang (Harvard) ):
(Zoom link: https://gatech.zoom.us/j/93227128162) Scalable Distributed Control and Reinforcement Learning for Multi-Agent Network Systems [ recording (Canvas) ] |
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10/16 | Project Proposal Due | |||
10/21 |
Lecture #17
( Hanjun Dai (Google DeepMind) Hanjun is a staff research scientist and research manager at Google DeepMind. He obtained his PhD from Georgia Institute of Technology. His research focuses on efficient generative modeling for text, image and structured data, and the corresponding fundamental algorithms in sampling and optimization. His research outcome has been adopted in OSS projects and launched in Google Workspace, Gemini and Cloud AI. His work has been recognized by the 2022 Google Research tech impact award, AISTATS 2016 best student paper and best workshop papers in Recsys and Neurips. He has also served as the Area Chair in top-tier conferences including AAAI, ICML, NeurIPS, co-organized workshops and tutorials in ICML, NeurIPS, LoG.):
(Zoom link: https://gatech.zoom.us/j/95094662178) Universal Query Engine [ recording (Canvas) ] |
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10/23 |
Lecture #18
(Bo Dai):
Representation Learning from EBM view [ notes ] |
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10/28 |
Lecture #19
(Bo Dai):
Representation Learning from Spectral Decomposition view [ notes ] |
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Module IV: Reinforcement Learning | ||||
10/30 |
Lecture #20
(Bo Dai):
MDP: Bellman Recursion [ notes ] |
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11/04 |
Lecture #21
(Bo Dai):
DP: Value and Policy Iteration [ notes ] |
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11/06 |
Lecture #22
(Bo Dai):
Learning with MDPs [ notes ] |
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11/11 |
Lecture #23
(Bo Dai):
Policy Gradient and Actor-Critic [ notes ] |
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11/13 |
Lecture #24
(Bo Dai):
RLHF in LLMs |
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11/18 |
Lecture #25
(Bo Dai):
Review |
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11/20 |
Lecture #26
:
Project Presentations (Zoom link: https://gatech.zoom.us/j/96090286355) [ recording (Canvas) ] |
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11/25 |
Lecture #27
:
Project Presentations (Zoom link: https://gatech.zoom.us/j/93702618622) [ recording (Canvas) ] |
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11/27 | No Class Student Recess | |||
12/02 |
Lecture #28
:
Project Presentations (Zoom link: https://gatech.zoom.us/j/98971277092) [ recording (Canvas) ] |
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12/09 | Project Report Due |