CS 2750 MACHINE LEARNING

Description

Minimum Credits: 3
Maximum Credits: 3
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)

Current Sections

Spring 2020

Class No.
Days
Times
Room
Instructor(s)
TA(s)
Type
Session
Writing
Class No.: 26685 (1010)
Days: TuTh
Times: 1:00 pm - 2:15 pm
Room: SENSQ 5313
Instructor(s): M. Hauskrecht
TA(s):
Type: LEC
Session: AT
Writing:

Past Sections

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