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.
|29984R||048||Quiz||TBA||TBA||412 of 600||OFFICE|
200 of 200
|30392R||048||Discussion||7:30-8:20pm||Tuesday||202 of 300||SGM123|
212 of 200
|30151R||048||Discussion||7:30-8:20pm||Thursday||210 of 300||SGM123|
|30259D||034||Lecture||5:00-7:20pm||Thursday||14 of 20||Victor Adamchik||DEN@Viterbi|
|30272R||034||Discussion||7:30-8:20pm||Thursday||14 of 20||DEN@Viterbi|
|29985R||034||Quiz||TBA||TBA||14 of 30||DEN@Viterbi|