Lectures for COMP-598: Topics in Computer Science: Applied Machine Learning

Fall 2014


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Schedule

Lec.
Date
Topic
Lecture Material
Homeworks and Readings
1
Sep. 2

Introduction to machine learning.
Slides Read this paper.
Review basic notions of algebra and probabilities.
Read Ch.1-2 of Bishop and Ch.1 of Hastie et al.
2
Sep.4

Linear regression.
Slides Suggested readings:
Ch.2 (Sec.2.1-2.4, 2.9) of Hastie et al.
Ch.3 of Bishop (Sec.3.1-3.2).
3
Sep. 9

Linear regression.
Slides
Mini-project #1 available.
Suggested readings:
Ch.3 (Sec.3.1-3.4, 3.9) of Hastie et al.
Ch.3 of Bishop (Sec.3.1-3.2).
4
Sep. 11

Linear classification.
Slides
Suggested readings:
Ch.4 of Hastie et al.
Ch.4 of Bishop (Sec.4.1-4.3).
A paper by Ng & Jordan (NIPS, 2001).
5
Sep. 16

Naive Bayes.
Slides Suggested readings:
Sec. 6.6.3 of Hastie et al.
Ch.13 (Sec.13.1-13.4) of the book Introduction to Information Retrieval.
6
Sep. 18

Performance analysis and error estimation.
Slides
Suggested readings:
Ch.7 of Hastie et al.
K. Wagstaff's (2012) paper
7
Sep. 23

Practical session with python and scikit-learn.
Mini-project #1 due.
8
Sep. 25

Decision trees
Slides
Mini-project #2 available.
Suggested readings:
Sec.14.4 of Bishop.
Sec.9.2 of Hastie et al.
9
Sep. 30

Instance-based learning
Slides Suggested readings:
Sec.2.5 of Bishop.
Sec.13.3 of Hastie et al.
10
Oct. 2

Ensemble methods
Slides
Suggested readings:
Sec.8.7, Ch.10 of Hastie et al.
Ch.14 of Bishop
11
Oct. 7

Ensemble learning (cont'd)
Slides
12
Oct. 9

Support vector machines
Slides Suggested readings:
Sec.7.1 of Bishop.
Ch.12 (Sec.12.1-12.4) of Hastie et al.
For background on convex optimization: see this book by S. Boyd and L. Vandenberghe
13
Oct. 14

Support vector machines (cont'd)
Slides
Mini-project #2 due.
14
Oct. 16

Neural networks
Slides
Mini-project #3 available.
Suggested readings
Ch.11 of Hastie et al.
Sec.5.1-5.3 of Bishop
15
Oct. 21

Feature construction and selection
16
Oct. 23

Unsupervised learning
17
Oct. 28

Deep learning
18
Oct. 30

Online / streaming data
19
Nov. 4

Parallelization for large-scale ML
Mini-project #3 due.
20
Nov. 6

Semi-supervised learning
Mini-project #4 out. (tentative date)
21
Nov. 11

Learning from crowd-sourcing
22
Nov. 13

Missing data.
23
Nov. 18

Midterm (confirmed date).
24
Nov. 20

Preference learning
25
Nov. 25

Recommendation systems
26
Nov. 27

Final project presentation session
Mini-project #4 due. (tentative date)
27
Dec. 2

Wrap-up