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
    30260D048Lecture5:00-7:20pmTuesday214 of 230Victor AdamchikSGM124feesession dates
    30297R048Discussion1:00-1:50pmThursday24 of 30OHE122session dates
    30299R048Discussion2:00-2:50pmWednesday35 of 40SOSB44session dates
    30300R048Discussion3:00-3:50pmWednesday
    40 of 40
    WPH207session dates
    30301R048Discussion4:00-4:50pmWednesday35 of 40WPH207session dates
    30302R001Discussion5:00-5:50pmWednesday24 of 40WPH207session dates
    30310R048Discussion12:00-12:50pmThursday18 of 30THH112session dates
    30311R048Discussion2:00-2:50pmThursday16 of 40WPH102session dates
    30312R048Discussion3:00-3:50pmThursday22 of 30WPH202session dates
    30265R048Quiz6:00-8:20pmWednesday214 of 230OFFICEsession dates
    30213D034Lecture5:00-7:20pmTuesday14 of 20Victor AdamchikDEN@Viterbifeesession dates
    30264R034Discussion1:00-1:50pmThursday14 of 20DEN@Viterbisession dates
    30266R034Quiz6:00-8:20pmWednesday14 of 20DEN@Viterbisession dates
    Information accurate as of May 22, 2019 5:12 pm.