Schedule
| Date | Lecture | Readings | Logistics | |
|---|---|---|---|---|
| Module 1: Foundations of PGMs and Exact Inference | ||||
| 1/21 |
Lecture #1
(Prof. Lengerich):
Course Introduction, Introduction to PGMs [ slides | notes ] |
|||
| 1/23 |
Lecture #2
(Prof. Lengerich):
Statistical Review & Maximum LIkelihood Estimation [ slides | notes ] |
HW1 Out |
||
| 1/28 |
Lecture #3
(Prof. Lengerich):
A Linear view of Discriminative and Generative Models [ slides | notes ] |
|
||
| 1/30 |
Lecture #4
(Prof. Lengerich):
Conditional Independence and Directed GMs (BNs) [ slides | notes ] |
|
HW2 Out |
|
| 2/4 |
Lecture #5
(Prof. Lengerich):
Undirected GMs (MRFs) [ slides | notes ] |
|
||
| 2/6 |
Lecture #6
(Prof. Lengerich):
Exact Inference 1 - Variable Elimination [ slides | notes ] |
|
||
| 2/11 | No class (skipped) | |||
| 2/13 | Quiz | |||
| Module 2: Learning | ||||
| 2/18 |
Lecture #7
(Prof. Lengerich):
Project Ideas + Learning Generalized Linear Models [ slides | notes ] |
|
||
| 2/20 |
Lecture #8
(Prof. Lengerich):
Parameter Learning in Fully-Observed BNs [ slides | notes ] |
|
HW2 Out |
|
| 2/25 |
Lecture #9
(Prof. Lengerich):
Learning Undirected GMs [ slides | notes ] |
|||
| 2/27 |
Lecture #10
(Prof. Lengerich):
Structure Learning [ slides | notes ] |
|||
| 3/4 |
Lecture #11
(Prof. Lengerich):
Causal Discovery [ slides | notes ] |
|||
| 3/6 |
Lecture #12
(Prof. Lengerich):
Parameter Learning of partially observed BNs [ slides | notes ] |
|
||
| 3/11 |
Lecture #13
(Prof. Lengerich):
Sequential Models [ slides | notes ] |
|
||
| 3/13 |
Lecture #14
(Prof. Lengerich):
Variational Inference, Monte Carlo [ slides | notes ] |
|
||
| 3/18 |
Lecture #15
(Prof. Lengerich):
Review [ slides | notes ] |
|||
| 3/20 | Exam | |||
| 3/25 | No class (Spring recess) | |||
| 3/27 | No class (Spring recess) | |||
| Module 3: Modern Probabilistic AI | ||||
| 4/1 |
Lecture #16
(Prof. Lengerich):
Deep Learning from a GM Perspective [ slides | notes ] |
|||
| 4/3 |
Lecture #17
(Prof. Lengerich):
CNNs, RNNs, Autoencoders [ slides | notes ] |
|||
| 4/8 |
Lecture #18
:
Deep Generative Models: GAN, VAEs [ slides | notes ] |
|||
| 4/10 |
Lecture #19
(Prof. Lengerich):
Attention and Transformers [ slides | notes ] |
|||
| 4/15 |
Lecture #20
(Prof. Lengerich):
LLMs from a Probabilistic Perspective 1: Implementing a GPT from Scratch [ slides | notes ] |
|||
| 4/17 |
Lecture #21
(Prof. Lengerich):
LLMs from a Probabilistic Perspective 2: Training on Unlabeled Data [ slides | notes ] |
|||
| 4/22 |
Lecture #22
(Prof. Lengerich):
LLMs from a Probabilistic Perspective 3: Fine-tuning on Labeled Data [ slides | notes ] |
|||
| 4/24 |
Lecture #23
(Prof. Lengerich):
Context-Adaptive Graphical Models [ slides | notes ] |
|||
| 4/29 | Project Presentations | |||
| 5/1 | Project Presentations | |||