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
    30352D048Lecture5:00-7:20pmWednesday258 of 281Haipeng LuoSGM123feesession dates
    30179R048Discussion11:00-11:50amFriday24 of 40Kim PetersKAP146session dates
    30182R048Discussion10:00-10:50amFriday31 of 40Kim PetersKAP144session dates
    30184R048Discussion1:00-1:50pmFriday22 of 33Kim PetersKAP144session dates
    30255R048Discussion2:00-2:50pmFriday33 of 40Kim PetersOHE100Bsession dates
    30334R048Discussion3:00-3:50pmTuesday35 of 40Victor AdamchikMHPB7Bsession dates
    30335R048Discussion4:00-4:50pmTuesday38 of 40Victor AdamchikMHPB7Bsession dates
    30336R048Discussion1:00-1:50pmTuesday39 of 40Victor AdamchikSOSB44session dates
    30338R048Discussion2:00-2:50pmTuesday36 of 40Victor AdamchikMHPB7Bsession dates
    29984R048QuizTBATBA258 of 331OFFICEsession dates
    30259D034Lecture5:00-7:20pmWednesday5 of 20Haipeng LuoDEN@Viterbifeesession dates
    30272R034Discussion2:00-2:50pmFriday5 of 20DEN@Viterbisession dates
    29985R034QuizTBATBA5 of 30OFFICEsession dates
    Information accurate as of January 19, 2019 8:06 am.