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.
|29995D||073||Lecture||5:00-7:20pm||Thursday||181 of 200||Haipeng Luo||ONLINE||PDF (51546 KB)|
|30151R||073||Discussion||7:30-8:20pm||Thursday||181 of 200||ONLINE|
|29984R||073||Quiz||TBA||TBA||181 of 200||ONLINE|
|30259D||034||Lecture||5:00-7:20pm||Thursday||6 of 20||Haipeng Luo||DEN@Viterbi|
|30272R||034||Discussion||7:30-8:20pm||Thursday||6 of 20||DEN@Viterbi|
|29985R||034||Quiz||TBA||TBA||6 of 20||DEN@Viterbi|