This course will give an overview of many techniques and algorithms in machine learning, beginning with topics such as linear and logistic regression, multi-layer neural networks and ending up with more recent topics such as boosting and support vector machines. The basic ideas and intuition behind modern machine learning methods, as well as, a more formal understanding of how and why they work will be covered. Students will have an opportunity to experiment with various machine learning techniques or apply them to a selected problem or domain in the context of a term project.

Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad LG/SNC Basis
Course Requirements: PLAN: Computer Science (CS-PHD; CS-MS; CSMSBS-MS) or Computer Engineering (COEAS-PHD; COEAS-MS; COEENG-PHD; COEENG-MCO)
Minimum Credits: 3
Maximum Credits: 3

Current Sections

Spring 2023

Class No.DaysTimesRoomInstructor(s)TA(s)Type
23954 (1010)TuTh11:00 am - 12:15 pmSENSQ 5313M. Hauskrecht