Email: |
xfern@eecs.oregonstate.edu |
Office: |
kelly 3073 |
Office hour: |
MWF 2-3pm, or by appointment |
Class
email list: |
cs434-f08@engr.oregonstate.edu |
Machine learning and Data mining is a subfield of artificial intelligence that develops computer programs that can learn from past experience and find useful patterns in data. This field has provided many tools that are widely used and making significant impacts in both industrial and research settings. Some of the application domains include personalized spam filters, HIV vaccine design, handwritten digit recognition, face recognition, credit card fraud detection, unmanned vehicle control, medical diagnosis, intelligent web search, etc.
This course will provide a basic introduction to this dynamic and fast advancing field. Topics include the three basic branches in this field: (1) Supervised learning for prediction problems (learn to predict); (2) Unsupervised learning for clustering data and discovering interesting patterns from data (learn to understand); and (3) Reinforcement learning for learning to select actions based on positive and negative feedback (learn to act). It will have a special focus on the practical side --- students will not only learn various machine learning and data mining techniques, but also learn how to apply them to real problems in practice.Date | Topics | Lecture Notes |
Reading |
Assignments |
---|---|---|---|---|
9/29 M |
Introduction to basic concepts | slides |
TM Chapter 1 | |
10/1 W |
The perceptron algorithm | slides | notes on perceptron by William Cohen | |
10/3 F |
The nearest neighbor algorithm |
Slides |
hw1, due monday 13th in class Solution |
|
10/6 M |
Decision tree algorithm |
slides |
J. R. Quinlan, Induction of decision trees, Machine learning 1: 81-106, 1986 |
|
10/8 W |
Decision tree cont. |
slides |
||
10/10 F |
Review of probability theory |
slides |
||
10/13 M |
(Naive) Bayes classifier |
slides |
hw2 due on Friday Oct 24th in class solution to the written part |
|
10/15 W |
NBC cont, logistic regression |
slides |
generative model vs discriminative model |
|
10/17 F |
Logistic Regression |
slides |
||
10/20 M |
Support Vector Machine |
slides |
Final project information |
|
10/22 W |
support vector machines cont. |
slides |
||
10/24 F |
Ensemble methods, bagging |
slides |
||
10/27 M |
boosting |
Slides |
A short introduction to boosting |
|
10/29 W |
Feature Selection |
slides |
||
10/31 F |
Clustering, HAC |
slides |
Assignment 3 Due Nov 12th |
|
11/3 M |
Clustering cont. Kmeans |
slides |
||
11/5 W |
midterm exam |
|||
11/7 F |
Gaussian Mixture modeling |
slides |
||
11/10 M |
Discussion of midterm questions |
|||
11/12 W |
Canceled class |
|||
11/14 F |
GMM cont, unsupervised dimension reduction |
slides |
Assignment 4, Due Nov 24th cluster.csv; random.csv |
|
11/17 M |
Guest lecture on sequence analysis |
|||
11/19 W |
Markov Decision Processes |
slides |
||
11/21 F |
MDPs cont. |
slides |
||
11/24 M |
Reinforcement learning |
slides |
hw5 : Due on 12/03 |
|
11/26 W |
Reinforcement learning - passive learning |
slides |
||
11/28 F |
No class - thanks giving holiday |
|||
12/1 M |
Reinforcement learning - active learning |
slides |
||
12/3 W |
Reinforcement learning - function approximation |
slides |
||
12/5 F |
Association rules mining |