COMP-551-002: Topics in Computer Science: Applied Machine Learning

Schedule for Winter 2018

The course schedule is not finalized at this point, and subject change.
Note that this is the schedule for section 002 only!

Lec.
Date
Topic
Lecture Material and Readings
Slides and Projects
1
Jan. 8

Introduction to machine learning.
Mandatory reading: This paper.
Suggested readings:
Bishop, Ch.1-2. Hastie et al., Ch.1. Shalev-Schwartz et al., Ch.2.
Slides
2
Jan. 10

Linear regression.
Suggested readings:
Bishop, Ch.3. Hastie et al., Ch.2 (Sec.2.1-2.4, 2.9).
Shalev-Schwartz et al., Ch.9
Slides
3
Jan. 15

Linear regression.

Suggested readings:
Ch.3 (Sec.3.1-3.4, 3.9) of Hastie et al.
Ch.3 of Bishop (Sec.3.1-3.2).
Ch.5 and 11 of Shalev-Schwartz
Mini-project 1 will be released this week
Slides
4
Jan. 17

Linear classification.

Suggested readings:
Ch.4 of Hastie et al.
Ch.4 of Bishop (Sec.4.1-4.3).
Sec.9.3 of Shalev-Schwartz
First tutorial this Friday January 19th, 6-7 pm, Stewart Biology S3/3!
Slides
Project 1 instructions
Data project 1
5
Jan. 22

Linear classification.

Suggested readings:
Sec. 6.6.3 of Hastie et al.
Ch.4 of Bishop (Sec.4.1-4.3).
Sec.24.1-24.3 Shalev-Schwartz
6
Jan. 24

Performance analysis and error estimation.

Suggested readings:
Ch.7 of Hastie et al.
Wagstaff (2012) paper
Mini-project 1 due on Jan. 26th
Mini-project 2 released
7
Jan. 29

Instance-based learning

Suggested readings:
Sec.2.5 of Bishop.
Sec.13.3 of Hastie et al.
Ch.19 of Shalev-Schwartz
8
Jan. 31

Support vector machines

Suggested readings:
Ch.7 of Bishop.
Ch.12 (Sec.12.1-12.4) of Hastie et al.
Ch.15 of Shalev-Schwartz
For more on convex optimization: see book by S. Boyd and L. Vandenberghe
9
Feb. 5

Support vector machines (cont'd)

Suggested readings:
See lecture 9.
10
Feb. 7

Decision trees

Suggested readings:
Sec.14.4 of Bishop.
Sec.9.2 of Hastie et al.
Mini-project 2 due Feb. 7th.
Mini-project 3 released.
11
Feb. 12

Ensemble methods

Suggested readings:
Sec.8.7, Ch.10 of Hastie et al.
Ch.14 of Bishop
Ch.10 of Shalev-Schwartz
12
Feb. 14

Unsupervised learning: Clustering
Suggested readings
Ch. 9 of Bishop
Ch. 14 of Hastier
Ch.22. of Shalev-Schwartz
13
Feb. 19

Unsupervised learning: Clustering
Suggested readings
Ch. 9 of Bishop
Ch. 14 of Hastier
Ch.22. of Shalev-Schwartz
Mini-project 3 due Feb. 19th
Project 4 released
14
Feb. 21

Neural networks

Suggested readings
Ch.11 of Hastie et al.
Ch.5 of Bishop
Ch.14 of Shalev-Schwartz
Ch.6 of Goodfellow et al.
15
Feb. 26

Neural networks (cont'd)
Suggested readings:
See lecture 14
16
Feb. 28

Deep learning
Suggested readings: Ch.9 of Goodfellow et al.
March 5

Study week - no class
March 7

Study week - no class
17
March 12

Deep learning (cont'd)
Suggested readings: Ch. 10 of Goodfellow et al.
18
March 14

Feature construction and selection
Suggested readings:
Ch. 12 of Bishop
Ch. 14 of Hastie
Ch. 23 of Shalev-Schwartz
Project 4 due March 15th
Final project released
19
March 19

Feature construction and selection
Suggested readings:
Ch. 12 of Bishop
Ch. 14 of Hastie
Ch. 23 of Shalev-Schwartz
20
March 21

Bayesian Inference
Suggested reading
Bishop 1.2.3, 1.2.6, 2.3.6
Possibly Shalev-Scharz 24.5, Hastie 8.3
21
March 26

Bayesian Linear Regression and Gaussian Processes
Suggested reading:
Bishop 3.3, 6.4.1, 6.4.2
Rasmussen & Williams, chapter 2
22
March 28

Gaussian Processes
Suggested reading:
Bishop 6.4.1, 6.4.2
Rasmussen & Williams, chapter 2
Apr. 2

Easter Monday - No class.
23
Apr. 4

No class. Midterm 5:30-8:30 pm (TBC) .
24
Apr. 9

Factor analysis
25
Apr. 11

Frontiers in machine learning
26
Apr. 16

Final project presentation session
Final project due Apr. 20th
-
TBD

Additional Final project presentation session