Time/Place: MW 10:25-11:40 in 301 GAVET
Instructor: Prof. Chenliang Xu
Instructor's Office Hours: MW 15:30-16:30 in 714 CSB
TA: Haofu Liao
TA's Office Hours: MW 12:30-13:30 in 603 CSB
The syllabus is here.
Probabilistic graphical models are a powerful framework that leverages probability theory and graph representation to model complex problems with large collections of inter-related variables. Numerous applications can be found in machine learning, computer vision and beyond. On the other hand, recent developments in neural networks (aka deep learning) have greatly advanced the performance of state-of-the-art in visual recognition and artificial intelligence. This course will cover the essentials (representation, learning and inference) in both probabilistic graphical models and neural networks with an emphasis in analyzing image and video data. Students entering the class should have a pre-existing working knowledge of probability, statistics, linear algebra and programming.
This is a tentative schedule of the course.
Course materials will be posted on Blackboard.
Green: lectures in DL;
Blue: lectures in PGM;
Red: other lectures;
Yellow: exam/presentation;
Grey: no class or cancelled class.
W | Date | Topic | Read | Notes |
---|---|---|---|---|
1 | 1/18 | Introduction | ||
2 | 1/23,25 | Foundations: Probability, Graph Theory, Programming | KF 2.1, 2.2 | |
3 | 1/30,2/1 | Linear Regression, Gradient Descent, Logistic Regression, Regularization | Optional: Andrew Ng's notes; Bishop 1.1, 3.1, 4.3 | |
4 | 2/6,8 | Feedforward, Backpropagation | GBC 6; UFLDL Tutorial | HW1 Out 2/6, Teaming Due 2/8 |
5 | 2/13,15 | CNN, Regularization | GBC 9, 7 | |
6 | 2/20,22 | Optimization, RNN | GBC 8, 10; H. Jaeger 2002 Ch 1, 2 | |
7 | 2/27,3/1 | LSTM, Autoencoders, Applications | GBC 14, 11, 12, 15 | |
8 | 3/6,8 | Representation: Bayesian Networks | KF 3, 5 | HW1 Due 3/6, Proposal Due 3/8, HW2 Out 3/10 |
9 | 3/13,15 | Spring Recess | ||
10 | 3/20,22 | Representation: Markov Networks | KF 4 | |
11 | 3/27,29 | Inference: Variable Elimination, Belief Propagation | KF 9, 10 | |
12 | 4/3,5 | Inference: Loopy BP, MAP Graph Cut | KF 11, 13 | HW3 Out 4/5 |
13 | 4/10 | Guest Lecture by Prof. Ji Liu | HW2 Due 4/10 (ext.) | |
13 | 4/12 | Inference: Sampling I | KF 12 | |
14 | 4/17, 19 | Inference: Sampling II; Learning: Parameter Learning in BNs | KF 17 | |
15 | 4/24, 26 | Learning: Parameter Learning in MNs; PGMs in Deep Learning | KF 20; GBC 20 | |
16 | 5/1 | Project Presentation (Group 1) | All slides due 5/1 8am | |
16 | 5/3 | Project Presentation (Group 2) | HW3 Due 5/3, Project Report Due 5/15 noon (ext.) |
Presentation Groups
Group 1: Bot01, Deep South Taco, Doge, Kicking Horse Mountain, Language X, Natty, Synapse, The Adversarial Learners, ZZG.
Group 2: AKI, CAL 9000, Connectome, HAHAHA, Halo, KW, Lorem Ipsum, Naive, Turtlebot.