Machine Learning (4.0 units)
Statistical methods for building intelligent and adaptive systems that improve performance from experiences; Focus on theoretical understanding of these methods and their computational implications. Recommended preparation: Undergraduate level training or coursework in linear algebra, multivariate calculus, basic probability and statistics; an undergraduate level course in Artificial Intelligence may be helpful but is not required.
- Crosslist: This course is offered by the CSCI department but may qualify for major credit in ISE. To register, enroll in CSCI 567.
|29907R||902||Lecture||11:00-1:50pm||MWTh||58 of 80||Victor Adamchik|
|29908R||902||Discussion||TBA||TBA||58 of 100||OFFICE|
|29909R||902||Quiz||TBA||TBA||58 of 100||OFFICE|
|29937D||910||Lecture||11:00-1:50pm||MWTh||1 of 30||Victor Adamchik||DEN@Viterbi|
|29938R||910||Discussion||TBA||TBA||1 of 30||DEN@Viterbi|
|29939R||910||Quiz||TBA||TBA||1 of 30||DEN@Viterbi|