|Location:||McConnell Engineering building, MC103|
|Times:||Monday / Wednesday, 2:30-4:00pm|
||Prof. Joelle Pineau, School of Computer Science
Office: McConnell 106N
Office hours: Mondays 1:00-2:00pm.
||Mahdi Milani Fard
Office: McConnell 112
Office hours: Tuesdays 4:00-5:00pm. In his office.
|Class web page:||http://www.cs.mcgill.ca/~jpineau/comp598|
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 2012 or before CANNOT take COMP-598. However students will be able to take both COMP-598 in Fall 2013 and COMP-652 in Winter 2014. Contents of both courses have been designed to avoid too much overlap.
The weekly quizzes will be short tests designed to assess basic understanding of the course material as we progress through the topics.
The assignments will require written work and programming to gain hands-on experience with the concepts covered in the lectures.
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 (November).
For the final project, students are expected to complete an in-depth analysis of a large dataset or recent machine learning algorithm. The students will be responsible for indentifying the problem and methods of analysis, and presenting the results of their work in writing and during an in-class presentation.
All assignments are INDIVIDUAL! You may discuss assignments with your colleagues, but you must prepare and submit assignments individually. You must acknowledge all sources (papers, code, books, websites, individual communications) using appropriate referencing style.
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.