COMP-551-001: Topics in Computer Science: Applied Machine Learning

Tentative Schedule for Winter 2018

Lec.
Date
Topic
Lecture Material
Readings
1
Jan. 9

Introduction to machine learning.
announcements, slides, notes, scribbles. Computing Machinery and Intelligence
Probability Review-1
Probability Review-2
Linear Algebra Review
2
Jan. 11

Introduction to machine learning.
announcements, notes, scribbles.
3
Jan. 16

Statistical decision theory.
notes, scribbles. Singular Value Decomposition.

Tutorial-1: 19 Jan 5pm-6pm at Stewart Biology S3/3.
4
Jan. 18

Linear regression.
notes
5
Jan. 23

Bias-variance tradeoff, Regularization
6
Jan. 25

Linear classification, indicator regression, LDA, QDA, Logistic regression.
7
Jan. 30

Percetron, separating hyperplane.
8
Feb. 1

SVM, Kernel Methods.
9
Feb. 6

Decision Trees.
10
Feb. 8

Ensemble Learning, Bagging, Boosting, Stacking.
11
Feb. 13

Neural Networks.
12
Feb. 15

Neural Networks.
13
Feb. 20

Optimization.
14
Feb. 22

Optimization.
15
Feb. 27

RNN, ConvNets.
16
Mar. 1

Bayesian Learning, Naive Bayes, MLE/MAP.
17
Mar. 6

No class.
18
Mar. 8

No class.
19
Mar. 13

MLE/MAP, Density Estimation.
20
Mar. 15

GMM, EM Algorithm.
21
Mar. 20

GMM, EM Algorithm.
22
Mar. 22

Bayesian Linear Regression, GP.
23
Mar. 27

GP, Unsupervised Learning.
24
Mar. 29

Clustering, K-Means, DBScan.
25
Apr. 3

No Class (Mid-term week).
26
Apr. 5

Dimensionality Reduction, PCA, T-SNE.
27
Apr. 10

Factor Analysis, ICA, CCA.
28
Apr. 12

Frontiers in ML, What Next?.