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

Fall 2014


Some lecture notes will be linked to this web page, in PDF format. The reader for PDF files is available free from Adobe for UNIX, Apple Macintosh, and Windows.

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

Neural networks (cont'd)
Slides
16
Oct. 23

Feature construction and selection
Slides
17
Oct. 28

Unsupervised learning
18
Oct. 30

Deep learning
19
Nov. 4

Online / streaming data
Mini-project #3 due.
20
Nov. 6

Parallelization for large-scale ML
Mini-project #4 out. (tentative date)
21
Nov. 11

Semi-supervised learning
22
Nov. 13

Learning from crowd-sourcing
23
Nov. 18

Midterm (confirmed date).
24
Nov. 20

Missing data.
25
Nov. 25

Recommendation systems / Preference learning
26
Nov. 27

Final project poster session
27
Dec. 2

Wrap-up
Mini-project #4 project report due.