COMP-551-001 Topics in Computer Science: Applied Machine Learning (Winter 2018)

General Information

When/Where:	Tuesdays 11:35am-12:55pm (Leacock 232) and Thursdays 11:35am-12:55pm (Leacock 232)
Instructor:	Sarath Chandar, [email protected] Office hours: Tuesdays 1:00pm-2:00pm, MC104N
Teaching assistants:	Philip Amortila, [email protected] Office hours: Mondays 2:30pm-3:30pm, MC106
	Christopher Glasz, [email protected] Office hours: Fridays 12:00pm-1:00pm, TR3104
	Prasanna Parthasarathi, [email protected] Office hours: Thursdays 1:00-2:00pm, MC108
	Koustuv Sinha, [email protected] Office hours: Wednesdays 4:00-5:00pm, TR3104
Course Schedule:	Click here.
Assignments:	Click here.
Announcements:	Click here.

Course Description

This is an introductory course in Machine Learning which covers fundamental topics in supervised learning and unsupervised learning.

Tentative Course content:

Introduction - Statistical Decision Theory - Linear Regression - Linear Classification - Indicator Regression - LDA - QDA - Logistic Regression - Perceptron - Separating Hyperplanes - Kernel Methods - SVM - K-NN - Decision Trees - ensemble learning - bagging - boosting - stacking - Neural Networks - RNNs - ConvNets - Backpropagation - Bayesian Learning - Naive Bayes - MLE/MAP - Density estimation - GMM - EM Algorithm - Bayesian Linear Regression - Gaussian Process - Unsupervised Learning - Clustering - K-means - DBScan - dimensionality reduction - PCA - T-SNE - Factor Analysis - ICA - CCA

Reference Materials

There is no required textbook. Lecture notes and references will be available from the course web page. The following texts can also be very useful:

Trevor Hastie, Robert Tibshirani and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition. Springer. 2009. Available free online.
Christopher Bishop. Pattern Recognition and Machine Learning. Springer. 2007.
Kevin Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press. 2012.
David MacKay. Information Theory, Inference and Learning Algorithms. Cambridge University Press. 2003.
Richard Duda, Peter Hard and David Stork. Pattern Classification. 2nd Edition. Wiley & Sons. 2001.
Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. The MIT Press. 2016. free online

Prerequisites / Anterequesites

Basic knowledge of a programming language is required. Basic knowledge of probabilities/statistics, calculus and linear algebra is required. Example courses at McGill providing sufficient background in probability are MATH-323 or ECSE-305. Some AI background is recommended, as provided, for instance by COMP-424 or ECSE-526, but not required. Note that while the course does not have strict prerequesites, it is a graduate-level course in computer science.

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.

Evaluation Criteria

The class grade will be based on the following components:

One in-class written midterm examination - 35%
3 Programming assignments - 30%
Kaggle competition - 15%
Reproducibility challenge project - 20%

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.

Evaluation Policy

Late work for programming assignments will be automatically subject to a 30% penalty, and can be submitted up to 1 week after the deadline.

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 www.mcgill.ca/students/srr/honest/ ) for more information).

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.