Syllabus for COMP 551-001: Applied Machine Learning

When: Winter 2020, Tuesday & Thursday, 11:30 am - 12:55 pm

Where: Strathcona Anatomy & Dentistry M-1 (notice the Feb 11 room change)

Jump to schedule for lectures | tutorials | midterm

Instructor: Reihaneh Rabbany: rrabba@cs.mcgill.ca {add COMP551 in the title}

Office hours: Thursday, 1:30 pm - 2:30 pm @ McConnell Engineering Building (MC) 232

Course website: this page [www.reirab.com/comp55120.html] for information and MyCourses for engagement [e.g. quizzes]

→ There is a second section offered for this course (COMP 551-002) by Prof. Siamak Ravanbakhsh.

Teaching assistants: {joint with the second section}

Name

Contact {@mail.mcgill.ca}

Office hours

Office location

Yanlin Zhang

yanlin.zhang2

Head TA

Jin Dong

jin.dong

Wed 3pm-4pm

McConnell 108

Haque Ishfaq

haque.ishfaq

Mon 1:00-2:00

McConnell 107

Martin Klissarov

martin.klissarov

Tue 3pm-4pm

McConnell 112

Kian Ahrabian

kian.ahrabian

Mon 5pm-6pm

McConnell 321

Yutong Yan

yutong.yan

Wed 2pm-3pm

McConnell 108

Samin Yeasar Arnob

samin.arnob

TBD

TBD

Tianzi Yang

tianzi.yang

Mon 4pm-5pm

Trottier 3090

Zhilong Chen

zhilong.chen

Tue 4pm-5pm

McConnell 229

David Venuto

david.venuto

Wed 1:00-2:00

McConnell 321

Arnab Kumar Mondal

arnab.mondal

Wed 11am-12pm

McConnell 202

FAQ

  • Will there be recordings? No, but you can refer to the slides and assigned readings
  • Will the two sections offer the same materials? That is the plan and assignments and mid-term will be jointly held, but the materials might or might not be covered in the same order, depth or pace.  
  • Course/waiting list is full, can I register? The limit is based on room capacity [we are already above that], unfortunately, there is little I can do. This course is offered every semester. Hopefully, you can register for the next term.
  • Do I meet the prerequisites? Please see below. If in doubt, see how well you follow the first few lectures.

Overview

This course covers a selected set of topics in machine learning and data mining, with an emphasis on good methods and practices for deployment of real systems. The majority of sections are related to commonly used supervised learning techniques, and to a lesser degree unsupervised methods. This includes fundamentals of algorithms on linear and logistic regression, decision trees, support vector machines, clustering, neural networks, as well as key techniques for feature selection and dimensionality reduction, error estimation and empirical validation.

Main Topics

Linear regression and linear classification

decision trees and ensemble methods

Support vector machines, kernels

Feature selection and dimensionality reduction

Performance evaluation, overfitting, cross-validation, bias-variance analysis, error estimation

Artificial neural networks and deep learning (e.g., RNNs and CNNs)

Unsupervised learning, clustering and Generative models

Please note that this syllabus is tentative and subject to change.

Relevant Textbooks

No required textbook but slides will cover chapters from the following books which can be used as reference materials.

[Bishop] Pattern Recognition and Machine Learning by Christopher Bishop (2007), available online 

[HTF] The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman (2009), available online

[Murphy] Machine Learning: A Probabilistic Perspective by Kevin Murphy (2012), available online through the library 

[GBC] Deep Learning  by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016), available online 

Prerequisites

This course requires programming skills (python) and basic knowledge of probabilities, calculus and linear algebra provided by courses similar to MATH-323 or ECSE-305. For more information see the course prerequisites and restrictions at McGill’s webpage.

Evaluation and Grading

The class grade will be based on the following components:

Weekly quizzes - 15% {online in mycourses} start on Jan 20th

Mini-projects - 50%  {group assignments}

Midterm examination - 35% {written on March 30th, 18:05-20:55, location: LEA 132 (section001) and ADAMs S AUD (section002)}

In the event of extraordinary circumstances beyond the University’s control, the evaluation scheme in a Course is subject to change, provided that there be timely communications to the students regarding the change.

Schedule {under construction}

† marks the slides that have been updated, please download again for correct copy.

Lectures

Date

Topic and Slides

Suggested Readings

Deadlines & deliverables

1

Tue., Jan. 7

Syllabus & Intro to ML

Bishop Ch. 1 & 2, Murphy Ch. 1., HTF Ch. 1, ML review paper by Domingos (2012)

2

Thu., Jan. 9

ML Basics

Same as above

3

Tue., Jan. 14

Linear regression

Bishop Ch. 3, HTF Ch. 2-3

4

Thu., Jan. 16

Linear classification

Bishop Ch. 4, HTF Ch. 4

5

Tue., Jan. 21

Tutorial in class

Math + Python

Mini-project 1 available

add/drop deadline (McGill calendar)

6

Thu., Jan. 23

Linear classification (cont.) & naive Bayes

Bishop Ch. 4, HTF Ch. 4

7

Tue., Jan. 28

Naive Bayes

Same as above

8

Thu., Jan. 30

9

Tue., Feb. 4

10

Thu., Feb. 6

11

Tue., Feb. 11

Mini-project 1 is due

Mini-project 2 available

12

Thu., Feb. 13

13

Tue., Feb. 18

14

Thu., Feb. 20

15

Tue., Feb. 25

16

Thu., Feb. 27

Mini-project 2 is due

Mini-project 3 is available

Tue., Mar. 3

Holiday break Mar. 2-6

17

Thu., Mar. 5

18

Tue., Mar. 10

19

Thu., Mar. 12

20

Tue., Mar. 17

Mini-project 3 is due

21

Thu., Mar. 19

22

Tue., Mar. 24

Mini-project 4 is available

23

Thu., Mar. 26

24

Tue., Mar. 31

25

Thu., Apr. 2

26

Tue., Apr. 7

27

Thu., Apr. 9

Mini-project 4 is due

Quick Notes

Tutorials {tentative and subject to change, exact dates TBD}

1

mid Jan.

Python

https://www.python.org/

2

end of Jan.

Scikit-learn

https://scikit-learn.org/

3

end of Feb.

PyTorch

https://pytorch.org/

Late Submissions

All due dates are 11:59 pm in Montreal unless stated otherwise. No make-up quizzes will be given. For mini-projects, late work will be automatically subject to a 20% penalty and can be submitted up to 5 days after the deadline.

If you experience barriers to learning in this course, submitting the projects, etc., please do not hesitate to discuss them with me. As a point of reference,  you can reach the Office for Students with Disabilities at 514-398-6009.

Academic Integrity

All students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Code of Student Conduct and Disciplinary Procedures. Please see www.mcgill.ca/students/srr/honest/ for more information.

Resources, relevant courses and useful links: