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.
|29936D||906||Lecture||9:00-10:30am||MTuW||77 of 120||Michael Shindler||OHE122||PDF (227954 KB)|
|29937R||906||Discussion||12:30-1:30pm||Tuesday||77 of 120||OHE136|
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|29938D||911||Lecture||9:00-10:30am||MTuW||18 of 20||Michael Shindler||DEN@Viterbi||PDF (227954 KB)|
|29939R||911||Discussion||12:30-1:30pm||Tuesday||18 of 20||DEN@Viterbi|
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