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Course Description6.036 introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction; formulation of learning problems; representation, over-fitting, generalization; clustering, classification, probabilistic modeling; and methods such as support vector machines, hidden Markov models, and Bayesian networks.
Prerequisites: 6.00 and 18.02 required; 6.006 and 18.06 recommended
LectureThe main lecture will take place on Tuesdays 9.30-11, in Room 32-123.
Section ScheduleThe sections this semester are:
- R05. Wednesday 11:00am-12:30pm
- R06. Wednesday 01:00pm-02:30pm
- R01. Friday 09:30am-11:00am
- R02. Friday 11:00am-12:30pm
- R03. Friday 01:00pm-02:30pm
- R04. Friday 02:30pm-04:00pm
Sections will all take place in Room 34-501. There is a nano-quiz at the start of each section meeting that can only be taken in class. If you are unable to attend due to illness or other personal difficulties, please see a dean in Student Support Services and ask them to contact Prof. Jacob White (firstname.lastname@example.org). Please do NOT email any of the course staff lists directly.
There is a checkoff with a member of staff that is due at the end of the section meeting. The checkoff can be made up (with a late penalty) at any office hours.
PiazzaYou are encouraged to join the course Piazza site where you can ask questions and get them answered by the course staff.
HomeworkHomework is due on-line Tuesday at 11 pm for students in sections that meet on Wednesday and due Thursday at 11 pm for students in sections that meet on Friday.
Office HoursOffice hours are an opportunity to get help with concepts or with particular assignments. Weekly office hours will be offered at the following times (subject to change):
- Sunday, 5-7pm
- Monday, 7-9pm
- Tuesday, 7-11pm
- Wednesday, 7-9pm
- Thursday, 7-11pm