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


CSE 250B: Principles of Artificial Intelligence: Learning

Winter 2010

Please ask questions on this message board.

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.  For registration, the section id of CSE 250B is 672198.  

Important note:  Registration is currently maxed out, at 48 students.  The limit is due to the number of seats in the classroom.  After some students drop, there
will likely be enough space for everyone who is interested.  Please email the instructor if you are interested and you have not been able to register.  All CSE students and all PhD students will be allowed in, regardless of enrollment limits.  If necessary, we will find a larger room.

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

Two important topics that will not be covered are graphical models and reinforcement learning.  The instructor is Charles Elkan, Professor.  For office hours, please send email to arrange an appointment.  

The only prerequisite for 250B is graduate status at UCSD, or consent of the instructor for undergraduates.  All CSE graduate students will be allowed into the course even if it is officially full.  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. 

LECTURES

Lectures will be on Tuesdays and Thursdays from 3:30pm to 4:50pm in the CSE building, room 2154.  For lecture notes from the Fall 2008 version of 250B, see http://www.cs.ucsd.edu/users/elkan/250Bfall2008.  The first lecture will be on Thursday January 5.

January 5
Topic
Assignment


TEXTBOOKS

The course will not be based on any single 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.  Examinations will be based mainly on the online lecture notes.

 

ASSIGNMENTS AND GRADING

Instead of a midterm exam, there will be a five-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 partner, 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.  Each pair of partners should hand in their joint report at the start of class on the day that the report is due.  Each 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 formComplete academic honesty is always required. 

The due dates for the four projects will be Thursday January 21, February 4, Tuesday February 23, and Thursday March 11.  The final exam will be on Tuesday March 16 at 3pm.  The last lecture will be on Thursday March 11.

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.  Instead, you should make an appointment to discuss with the instructor how you can do better on following projects.




Most recently updated on Novmber 5, 2009 by Charles Elkan, elkan@cs.ucsd.edu.