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.
|30079D||048||Lecture||10:00-11:50am||Wed, Fri||82 of 90||Yan Liu||OHE132|
|30081R||048||Discussion||TBA||TBA||82 of 90||OFFICE|
|30265R||048||Quiz||TBA||TBA||82 of 90||OFFICE|
|30213D||034||Lecture||10:00-11:50am||Wed, Fri||15 of 30||Yan Liu||DEN@Viterbi|
|30264R||034||Discussion||TBA||TBA||15 of 30||DEN@Viterbi|
|30266R||034||Quiz||TBA||TBA||15 of 30||DEN@Viterbi|