CS 6347: Statistical Methods in AI and ML
Spring 2019Course Info
Where: ECSN 2.120
When: MW, 11:30am-12:45pm
Instructor: Nicholas Ruozzi
Office Hours: M. 1pm-2pm, W. 10am-11am, and by appointment in ECSS 3.409
TA: TBD
Office Hours: TBD
Grading: problem sets (70%), final project (25%), class participation & extra credit (5%)
Attendance is MANDATORY. The instructor reserves the right to lower final grades as a result of poor attendance.
Prerequisites: some familiarity with basic probability, linear algebra, and introductory machine learning (helpful, but not required).
When: MW, 11:30am-12:45pm
Instructor: Nicholas Ruozzi
Office Hours: M. 1pm-2pm, W. 10am-11am, and by appointment in ECSS 3.409
TA: TBD
Office Hours: TBD
Grading: problem sets (70%), final project (25%), class participation & extra credit (5%)
Attendance is MANDATORY. The instructor reserves the right to lower final grades as a result of poor attendance.
Prerequisites: some familiarity with basic probability, linear algebra, and introductory machine learning (helpful, but not required).
Schedule & Lecture Slides
Week | Dates | Topic | Readings |
1 | Jan. 14 & 16 | Introduction & Basic Probability Bayesian Networks | K&F: Ch. 1 & 2 Basic Probability BN Notes |
2 | Jan. 23 | More BNs: D-separation | K&F: Ch. 3 Octave (free version of MATLAB) |
3 | Jan. 28 & 30 | Markov Random Fields | K&F: Ch. 4 & Ch. 9 Darwiche: Ch. 9 MRF Notes |
4 | Feb. 4 & Feb. 6 | Variable Elimination & BP Approx. MAP Estimation MAP LP | K&F: 13.1-13.5, A.5.3 Boyd: Ch. 5.1-5.5 |
5 | Feb. 11 & 13 | More Approximate MAP Variational Methods | Approximate MAP Notes K&F 11.1-11.2, 11.5 Sections 1-3 of this paper |
6 | Feb. 18 & 20 | Intro to Sampling | K&F 12.1-12.3 |
7 | Feb. 25 & 27 | Markov Chain Monte Carlo Intro to Machine Learning | K&F: 17.1-17.4 |
8 | Mar. 4 & Mar. 6 | MLE for BNs and Log-Linear Models | K&F: 20.1-20.5 |
9 | Mar. 11 & Mar. 13 | More MLE Alternatives to MLE | |
10 | Mar. 18 & Mar. 20 | Expectation Maximization Hidden Markov Models | K&F: 19.1-19.2 Box 17.E |
11 | Mar. 25 & Mar. 27 | Structure Learning LDA | K&F: 20.6 |
12 | Apr. 1 & Apr. 3 | Exponential Families and EP | |
13 | Apr. 8 & Apr. 10 | Free Energy Approximations | |
14 | Apr. 15 & Apr. 17 | Causal Inference
Graph Cuts and Efficient MAP inference | Ising Models General Models |
Problem Sets
All problem sets will be available on the eLearning site and are to be turned in there. See the homework guidelines below for the homework policies.
Textbooks & References
This semster, online notes in book form will (hopefully) be available for each lecture. In addition, the following textbook is suggested:
- Probabilistic Graphical Models: Principles and Techniques, by Daphne Koller and Nir Friedman.
- Modeling and Reasoning with Bayesian Networks, by Adnan Darwiche.
- Machine Learning: a Probabilistic Perspective, by Kevin Murphy.
Homework Guidelines*
We expect you to try solving each problem set on your own. However, when being stuck on a problem, I encourage you to collaborate with other students in the class, subject to the following rules:
- You may discuss a problem with any student in this class, and work together on solving it. This can involve brainstorming and verbally discussing the problem, going together through possible solutions, but should not involve one student telling another a complete solution.
- Once you solve the homework, you must write up your solutions on your own, without looking at other people's write-ups or giving your write-up to others.
- In your solution for each problem, you must write down the names of any person with whom you discussed it. This will not affect your grade.
- Do not consult solution manuals or other people's solutions from similar courses - ask the course staff, we are here to help!
*adpated from David Sontag