|When/Where:||Tuesdays 11:35am-12:55pm (Leacock 232) and Thursdays 11:35am-12:55pm (Leacock 232)|
||Sarath Chandar, firstname.lastname@example.org
Office hours: Tuesdays 1:00pm-2:00pm, MC104N
||Philip Amortila, email@example.com
Office hours: Mondays 2:30pm-3:30pm, MC106
|Christopher Glasz, firstname.lastname@example.org
Office hours: Fridays 12:00pm-1:00pm, TR3104
|Prasanna Parthasarathi, email@example.com
Office hours: Thursdays 1:00-2:00pm, MC108
|Koustuv Sinha, firstname.lastname@example.org
Office hours: Wednesdays 4:00-5:00pm, TR3104
|Course Schedule:||Click here.|
This is an introductory course in Machine Learning which covers fundamental topics in supervised learning and unsupervised learning.
Students who took COMP-652 in Winter 2013 or before CANNOT take COMP-551. Starting in Fall 2013, COMP-551 and COMP-652 were designed to avoid significant overlap; you can take either or both.
The courses is intended for hard-working, technically skilled, highly motivated students. Participants will be expected to display initiative, creativity, scientific rigour, critical thinking, and good communication skills.
The midterm is designed to assess in-depth understanding of fundamental methods and algorithms. It will be scheduled towards the later end of the semester. There is no final exam.
No make-up quizzes or midterm will be given.
Some of the course work will be individual, other components can be completed in groups. It is the responsibility of each student to understand the policy for each work, and ask questions of the instructor if this is not clear. It is also the responsibility of each student to carefully acknowledge all sources (papers, code, books, websites, individual communications) using appropriate referencing style when submitting work.
We will use automated systems to detect possible cases of text or software plagiarism. Cases that warrant further investigation will be referred to the university disciplinary officers. Students who have concerns about how to properly use and acknowledge third-party software should consult the course instructor or TAs.
McGill University values academic integrity. Therefore all students must understand
the meaning and consequences of cheating, plagiarism and other academic offences
under the Code of Student Conduct and Disciplinary Procedures (see
In accord with McGill University's Charter of Students' Rights, students in this course have the right to submit in English or in French any written work that is to be graded.
In the event of extraordinary circumstances beyond the University's control, the content and/or evaluation scheme in this course is subject to change.