CS542 - Machine Learning

Announcements

• 1/18/12 - Welcome to the course! Here is the syllabus.
• 1/21/12 - Problem Set 0 is available.
• 1/24/12 - Problem Set 1 is available. It is due February 3, 2012 at 4p.
• 1/25/12 - Problem Set 0 solutions are available.
• 2/7/12 - Problem Set 2 is available.
• 2/7/12 - Kevin's office hours for today are cancelled and have been rescheduled for next Tuesday. Therefore, next Tuesday, Kevin's office hours are from 2:30p-5:45p
• 2/19/12 - Problem Set 3 is available.
• 2/20/12 - Problem Set 1 solutions are available.
• 2/26/12 - Problem Set 2 solutions are available.
• 3/5/12 - Problem Set 3 solutions are available.
• 3/20/12 - A preliminary version of Problem Set 4 is available. This will be finalized by 3/21/12.
• 3/21/12 - Problem Set 4 is available.
• 3/22/12 - Problem Set 3b is available.
• 3/22/12 - Midterm Solutions are available.
• 3/26/12 - Problem Set 3b with (minor) typo fixes is available.
• 3/30/12 - Problem Set 4 with (minor) typo fixes (largely regarding threshold in algorithms) is available.
• 4/3/12 - Problem Set 5 is available.
• 4/17/12 - Problem Set 6 is available.
• 4/18/12 - Solutions for Problem Set 3b are available.
• 4/21/12 - Solutions for Problem Set 5 are available.
• 4/29/12 - Final Project Guidelines are available. Although stated in the guidelines, I want to emphasize that these are only guidelines and your report should be tailored to your project.
• 5/3/12 - Solutions for Problem Set 6 are available.
• 5/11/12 - Solutions for the Final Exam are available.

Instructor: Kevin Small ()
Office: MCS 146
Office Hours: Mondays, 6:30p-7:45p; Tuesdays, 2:30p-4:30p (or by appointment)

TF: Zhiqiang (Alex) Ren ()
Office: Undergraduate Lab
Office Hours: Tuesdays, 4:00p-6:00p, Thursdays, 4:00p-5:30p

Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.

Most information can be found on the schedule.

Problem Sets (6)	35%
Project	15%
Midterm Exam	20%
Final Exam	30%