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CS 434: Machine Learning and Data Mining

 Fall  2013

MWF 12:00 - 12:50 Kelly 1003

 

 


 

Instructor: Dr. Xiaoli Fern

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.

Syllabus

Course Policy


Course materials


Prerequisites:

Students are expected to have the following background:

 


Learning objectives

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.

 


Lecture Schedule

Week 1 (9/30/13-10/4/13) (Reading assignment: Read the review slides posted under Prerequisites above)


Assignments:


Week 2 (10/7/13-10/11/13)

    Assignments:


Week 3 (10/14/13-10/18/13)

    Assignment:

Week 4 (10/21/13 - 10/25/13)

Assignment:

Week 5 (10/28/13 -11/1/13)

Assignment:

Week 6 (11/4/-11/8)

Assignment:

Week 7 (11/11-11/15)

Week 8 (11/18- 11/22)

Assignment:

Week 9 (11/25-11/27)

Week 10 (12/2 - 12/6)