Computer Science 567:

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.
    SectionSessionTypeTimeDaysRegisteredInstructorLocationSyllabusInfo
    30352D048Lecture5:00-6:20pmTue, Thu215 of 220Fei ShaSGM124PDF (87856 KB)feesession dates
    30179R048Discussion6:30-7:20pmFriday30 of 33THH213session dates
    30182R048Discussion6:30-7:20pmMonday30 of 33THH213session dates
    30184R048Discussion7:30-8:20pmMonday24 of 33THH213session dates
    30185R048Discussion7:30-8:20pmFriday21 of 33THH213session dates
    30255R048Discussion8:00-8:50amTuesday24 of 30Michael ShindlerOHE122session dates
    30256R048Discussion9:00-9:50amTuesday32 of 33Michael ShindlerKAP140session dates
    30257R048Discussion9:00-9:50amMonday26 of 31Michael ShindlerVHE206session dates
    30258R048Discussion10:00-10:50amMonday28 of 30Michael ShindlerVHE206session dates
    30259D034Lecture5:00-8:00pmTue, Thu12 of 20Fei ShaDEN@Viterbifeesession dates
    30272R034Discussion8:00-8:50amTuesday12 of 20Michael ShindlerDEN@Viterbisession dates
    Information accurate as of March 4, 2018 4:29 pm.