Machine Learning

CAS CS 542

Prerequisites: Programming (CASCS112 or equivalent), Linear Algebra (CASCS132 or equivalent), Probability (CASCS237 or equivalent), and single-variable calculus (MA 123-124 or equivalent); multi-variable calculus (MA 225 or equivalent) is highly recommended. 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.

FALL 2018 Schedule

Section Instructor Location Schedule Notes
A1 Saenko CAS B12 TR 2:00 pm-3:15 pm Class Closed

FALL 2018 Schedule

Section Instructor Location Schedule Notes
A2 Saenko EPC 203 W 9:05 am-9:55 am Class Closed

FALL 2018 Schedule

Section Instructor Location Schedule Notes
A3 Saenko KCB 102 W 10:10 am-11:00 am Class Closed

FALL 2018 Schedule

Section Instructor Location Schedule Notes
A4 Saenko CAS 116 W 1:25 pm-2:15 pm Class Closed

FALL 2018 Schedule

Section Instructor Location Schedule Notes
A5 Saenko CAS 116 W 3:35 pm-4:25 pm Class Closed

SPRG 2019 Schedule

Section Instructor Location Schedule Notes
A1 Chin TR 5:00 pm-6:15 pm

SPRG 2019 Schedule

Section Instructor Location Schedule Notes
A2 Chin F 9:05 am-9:55 am

SPRG 2019 Schedule

Section Instructor Location Schedule Notes
A3 Chin F 10:10 am-11:00 am

SPRG 2019 Schedule

Section Instructor Location Schedule Notes
A4 Chin F 11:15 am-12:05 pm

Note: this course is also offered during Summer Term

Note that this information may change at any time. Please visit the Student Link for the most up-to-date course information.