CS 6347: Statistical Methods in AI and MLSpring 2019
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).
Schedule & Lecture Slides
|1||Jan. 14 & 16||Introduction & Basic Probability Bayesian Networks||K&F: Ch. 1 & 2Basic Probability|
|2||Jan. 23||More BNs: D-separation||K&F: Ch. 3Octave (free version of MATLAB)|
|3||Jan. 28 & 30||Markov Random Fields||K&F: Ch. 4 & Ch. 9 Darwiche: Ch. 9MRF Notes|
|4||Feb. 4 & Feb. 6||Variable Elimination & BPApprox. MAP EstimationMAP LP||K&F: 13.1-13.5, A.5.3Boyd: Ch. 5.1-5.5|
|5||Feb. 11 & 13||More Approximate MAPVariational Methods||Approximate MAP Notes K&F 11.1-11.2, 11.5Sections 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 MLEAlternatives to MLE|
|10||Mar. 18 & Mar. 20||Expectation Maximization Hidden Markov Models||K&F: 19.1-19.2Box 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|
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.
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