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.
8
Sep. 25

Decision trees
9
Sep. 30

Instance-based learning
10
Oct. 2

Support vector machines
11
Oct. 7

Support vector machines (cont'd)
12
Oct. 9

Neural networks
13
Oct. 14

Ensemble methods
14
Oct. 16

Feature construction and selection
15
Oct. 21

Dataset analysis
16
Oct. 23

Unsupervised learning
17
Oct. 28

Deep learning
18
Oct. 30

Online / streaming data
19
Nov. 4

Parallelization for large-scale ML
20
Nov. 6

Midterm (tentative date)
21
Nov. 11

Learning from crowd-sourcing
22
Nov. 13

Semi-supervised learning
23
Nov. 18

Missing data.
24
Nov. 20

Recommendation systems
25
Nov. 25

Final project presentation session
26
Nov. 27

Wrap-up