|When/Where:||Mondays 1-2:30pm (Leacock 26) and Wednesdays 8:30-10am (MC304)|
||Prof. Joelle Pineau, email@example.com
Office hours: Wednesdays 10-11am, MC106N
||Dr. Herke van Hoof, firstname.lastname@example.org
Office hours: By appointment only, MC104N
||Philip Amortila, email@example.com
Office hours: Thursdays 10-11am, MC106
|Christopher Glasz, firstname.lastname@example.org
Office hours: Wednesdays 3-4pm, TR3104
|Harsh Satija, email@example.com
Office hours: Mondays 10:30-11:30am, MC106
|Koustuv Sinha, firstname.lastname@example.org
Office hours: Wednesdays 4-5pm, TR3090
|Matthew Smith, email@example.com
Office hours: Tuesdays 2-3pm, MC111
|Sanjay Thakur, firstname.lastname@example.org
Office hours: Fridays 10:30-11:30am, MC105
|Class web page:||http://www.cs.mcgill.ca/~jpineau/comp551|
The course will cover selected topics and new developments in Data mining and Machine learning, with a particular emphasis on good methods and practices for effective deployment of real systems. We will study commonly used algorithms and techniques, including clustering, neural networks, support vector machines, decision trees. We will also discuss methods to address practical issues such as feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large datasets.
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 weekly exercises will consist of quizzes (in class) or practical work (take-home) designed to develop basic understanding of the course material as we progress through the topics. These are designed to provide some practice for the midterm.
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 (mid-November). There is no final exam.
The projects will require reading, writing, programming and experiments to gain hands-on experience with the application of recent machine learning methods, including concepts covered in the lectures, and concepts drawn from the literature. Students will be responsible for characterizing the problem, developing methods of analysis, and presenting the results of their work. Some projects may be individual, most will be done in groups (usually of 3 students).
We will use a peer-review system to evaluate the data analysis case studies. Each student will be asked to read and evaluate submissions of their colleagues. The emphasis will be placed on providing constructive feedback on the methodology and presentation.
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.