DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
UNIVERSITY OF CALIFORNIA, SAN DIEGO


CSE 250B: Principles of Artificial Intelligence: Learning

Winter 2012


Please ask questions using Piazza. Lectures are in WLH 2207 on Tuesdays and Thursdays at 11am. Please arrive in class on time. There will be a quiz at the start of class every Thursday, beginning on January 12.


OVERVIEW

CSE 250B is a graduate course devoted to the basic concepts and algorithms of supervised and unsupervised learning from data. 250B is open to Ph.D. and M.S. students in computer science, engineering, cognitive science, and related areas. Other prospective participants, including enthusiastic undergraduates, are welcome, but should contact the instructor at elkan@cs.ucsd.edu. The prerequisite for 250B is graduate status at UCSD, or consent of the instructor for undergraduates. CSE 250A (taught by Prof. Lawrence Saul) and 250B are complementary. Students may take one or both courses: neither is a prerequisite for the other, and there will be little overlap.

The specific topics discussed in CSE 250B will include, not necessarily in this order,

The instructor is Charles Elkan, Professor. For office hours, please send email to arrange an appointment. The teaching assistant is Aditya Menon, whose office hours will be Wednesdays from 10am to 12pm in the basement room B240A in the CSE building.

LECTURES

Lectures will be on Tuesdays and Thursdays from 11am to 12:20pm in Warren Lecture Hall, room 2207. For lecture notes from the previous version of 250B, see http://www.cs.ucsd.edu/users/elkan/250Bwinter2011. The last lecture will be on Thursday March 15.

January 10
 Maximum likelihood (ML), maximum log conditional likelihood
January 12
 Logistic regression model, partial derivatives
January 17
 convex minimization, stochastic gradient (SG) in general
January 19
 SG algorithm, cross-validation
January 24
 Log-linear models, feature functions
January 26
 Conditional random fields (CRFs)
January 31
 Viterbi algorithm for CRFs
February 2
 Gradients for CRFs
February 7
 Forward and backward vectors
February 9
 Gibbs sampling and contrastive divergence for CRFs
February 14
 Text mining, bag of words representation, multinomial urn process
February 16
 Multinomial and Dirichlet distributions, latent Dirichlet allocation (LDA)generative process
February 21
 LDA training via Gibbs sampling
February 23
 Probability equation for Gibbs sampling
February 28
 Representing meaning, recursive autoencoders (RAEs) for sentences
March 6
 RAEs and greedy search for tree structure
March 8
March 13
March 15
March 22
 Thursday at 11:30am: Final exam and last project report due.

Clickable links in the table above are to lecture notes in PDF. Notes for days or topics without a link are included in the notes for an earlier topic. The quizzes and assignment descriptions are included in the PDF lecture notes.

TEXTBOOKS

The course will not be based on a book.   The following textbooks are recommended as references:
For a price comparison among web booksellers use addall.com with the ISBN numbers.  Some topics discussed in class will not be in any textbook, and many will be explained differently, so coming to lectures and taking notes carefully is important. Quizzes and the final exam will be based on the online lecture notes.

ASSIGNMENTS AND GRADING

Instead of a midterm exam, there will be a seven-minute in-class quiz at the start of every Thursday lecture (10% of your overall grade), a final examination (30%), and four project assignments (15% each). You should do each project with one or two partners, so individual work will count for 40% of your grade and joint work for 60%. You are free to change partners, or not, between projects. 

Each project will last between two and three weeks and will require coding, experimenting with data, and writing a report. Using a high-level environment such as Matlab or R is encouraged. Projects will be graded based exclusively on the written report; see this writing advice. Each team should hand in its joint report at the start of class on the day that the report is due. Each day or part of a day that a report is late will cost 20% of the maximum score available for the project. Reports will be evaluated using grading criteria similar to those in this form. Complete academic honesty is always required. 

The first project will be handed out on January 12, and will be due back on Thursday January 26, 2012. Later projects will be due on Thursday February 16, Tuesday March 6, and Thursday March 22. The final exam will be on Thursday March 22, 2012, at 11:30am.

There is no a priori correspondence between letter grades and numerical scores on the assignments or on the exam. You can evaluate your performance in the class by comparing your scores with the means and standard deviations, which will be announced. However there is also no fixed correspondence between letter grades and standard deviations above or below the mean. If all students do well in the absolute, then all students will get a good grade.  

You should not drop CSE 250B just because you are unhappy with the score that you receive on a project or quiz. Instead, you should make an appointment to discuss with the TA or the instructor how you can do better on following projects and quizzes.


Most recently updated on March 9, 2012 by Charles Elkan, elkan@cs.ucsd.edu.