Email: |
xfern at eecs.oregonstate.edu |
Office: |
kelly 3073 |
Office hour: |
MWF 11-12pm, or by appointment |
Class email list: |
cs434-f13 at engr.oregonstate.edu |
TA:
Zahra Iman (zahra.iman87 at gmail.com)
Office hour: Tuesday 3-5 in Kelly atrium
Machine learning and Data mining is a subfield of artificial intelligence that develops computer programs that can learn from past experience (learning) and find useful patterns in data (mining). 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, recommender systems 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.
Upon
completing
the course, students are expected to:
1)
be able to apply supervised
learning algorithms to prediction problems and evaluate
the results.
2)
be able to apply
unsupervised learning algorithms to data analysis
problems and evaluate results.
3)
be able to apply
reinforcement learning algorithms to control problem and
evaluate results.
4)
be able to take a
description of a new problem and decide
what kind of problem (supervised, unsupervised,
or reinforcement) it is.
Week 1 (9/30/13-10/4/13) (
Assignments:
Week 2 (10/7/13-10/11/13)
Assignments: