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

Fall 2013


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 Slides
To-do
1
Sep. 4

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. 9

Linear regression.
Slides Suggested readings:
Ch.2 (Sec.2.1-2.4, 2.9) of Hastie et al.
Read Ch.3 of Bishop.
3
Sep. 11

Linear regression.
4
Sep. 16

Linear classification.
5
Sep. 18

Performance analysis and error estimation.
6
Sep. 23

Dataset analysis
7
Sep. 25

Kernel smoothing and Naive bayes
8
Sep. 30

Decision trees
9
Oct. 2

Support vector machines
10
Oct. 7

Ensemble methods
11
Oct. 9

Neural networks
Oct. 14

Thanksgiving Holiday
12
Oct. 16

Feature selection
13
Oct. 21

Regularization and dimensionality reduction
14
Oct. 23

Deep learning
15
Oct. 28

Cost-sensitive learning
16
Oct. 30

Online / streaming data
17
Nov. 4

Data structures and Map-Reduce
18
Nov. 6

Nearest neighbor methods
19
Nov. 11

Unsupervised learning
20
Nov. 13

Semi-supervised learning
21
Nov. 18

Recommendation systems
22
Nov. 20

Ranking and preference learning
23
Nov. 25

Applications
24
Nov. 27

Applications
25
Dec. 2

Project presentations
26
Dec. 3

Wrap-up