CS 6347: Statistical Methods in AI and ML

Spring 2017

Course Info

Where: ECSN Building 2.203
When: MW, 11:30am-12:45pm

Instructor: Nicholas Ruozzi
Office Hours: Tuesday/Friday 10am-11am and by appointment in ECSS 3.409

TA: Dhruv Patel
Office Hours: M/W 4pm - 5pm in ECSS 2.104A1

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. 9 & 11 Introduction & Basic Probability
Bayesian Networks
K&F: Ch. 1 & 2
Basic Probability
BN Notes
2 Jan. 18 More BNs: D-separation
K&F: Ch. 3
Octave (free version of MATLAB)
3 Jan. 23 & 25 Markov Random Fields
K&F: Ch. 4 & Ch. 9
Darwiche: Ch. 9
MRF Notes
4 Jan. 30 & Feb. 1 Variable Elimination & BP
Approx. MAP Estimation
K&F: 13.1-13.5, A.5.3
Boyd: Ch. 5.1-5.5
5 Feb. 6 & 8 More Approximate MAP
Variational Methods
Approximate MAP Notes
K&F 11.1-11.2, 11.5
Sections 1-3 of this paper
6 Feb. 13 & 15 Intro to Sampling
Markov Chain Monte Carlo
K&F 12.1-12.3
7 Feb. 20 & 22 Intro to Machine Learning
Maximum Likelihood for Bayesian Networks
K&F: 17.1-17.4
8 Feb. 27 & Mar. 1 MLE for Log-Linear Models
K&F: 20.1-20.5
9 Mar. 6 & Mar. 8 Alternatives to MLE
Expectation Maximization
10 Mar. 20 & Mar. 22 Hidden Markov Models
Structure Learning
K&F: 19.1-19.2
Box 17.E
11 Mar. 27 & Mar. 29 LDA
Exponential Families and EP
K&F: 20.6
12 Apr. 3 & Apr. 5 Free Energy Approximations
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: Other references that may be helpful:

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:
  1. 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.
  2. 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.
  3. 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.
  4. Do not consult solution manuals or other people's solutions from similar courses - ask the course staff, we are here to help!
Late homeworks will NOT be accepted except in extreme circumstances or those permitted by university policy (e.g., a religious holiday). All such extensions MUST be cleared in advance of the due date.

*adpated from David Sontag