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CS671 Machine Learning

by Kee-Eung Kim last modified Jun 21, 2011 02:18 PM


Students must check the moodle page regularly for up-to-date information.


This course is an introductory graduate-level course on machine learning, a subfield of artificial intelligence. The goal is to provide a general introduction to machine learning, and to understand the important modeling techniques and the associated algorithms used in the core machine learning research areas. Taking CS570 prior to this course is encouraged but not required. There will be a set of small programming projects and a final exam.


Primary textbook
Alpaydin, "Introduction to Machine Learning"
Supplementary textbooks
Bishop, "Pattern Recognition and Machine Learning" 
Duda, Hart & Stork, "Pattern Classification"

Lecture Schedule

10:00-11:00 MWF CS Lecture Room #4 (CS #2445)

Office Hours (By Appointment Only)

15:00-16:00 MW in the instructors office (CS #2402)


Kee-Eung Kim  (김기응; Instructor)
Jaedeug Choi (최재득; TA)
Jehyun Jung (정제현; TA)

Grading Policy

Class Participation: 10%
Programming Projects (7): 70%
Final Exam: 20%