CS 2750 MACHINE LEARNING
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. | Days | Times | Room | Instructor(s) | TA(s) | Type |
---|---|---|---|---|---|---|
23954 (1010) | TuTh | 11:00 am - 12:15 pm | SENSQ 5313 | M. Hauskrecht | LEC |