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
    29984R048QuizTBATBA406 of 600OFFICEsession dates
    30352D048Lecture5:00-7:20pmTuesday198 of 200Victor AdamchikSGM123PDF (254658 KB)feesession dates
    30392R048Discussion7:30-8:20pmTuesday199 of 300SGM123session dates
    29995D048Lecture5:00-7:20pmThursday
    208 of 200
    Victor AdamchikSGM123PDF (254658 KB)feesession dates
    30151R048Discussion7:30-8:20pmThursday207 of 300SGM123session dates
    30259D034Lecture5:00-7:20pmThursday10 of 20Victor AdamchikDEN@ViterbiPDF (254658 KB)feesession dates
    30272R034Discussion7:30-8:20pmThursday10 of 20DEN@Viterbisession dates
    29985R034QuizTBATBA10 of 30DEN@Viterbisession dates
    Information accurate as of March 9, 2020 7:44 am.
    All Summer 2020 courses will be taught remotely. Faculty will contact students to provide information to login to classes.