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.

- No
text book required, lecture notes
and reading materials will be posted on the webpage, please
check regularly.
- Useful
References:
*Machine learning*, Tom Mitchell, McGraw-Hill 1997 (Referred to as TM).*Machine learning and pattern recognition*, Chris Bishop, Springer (Referred to as Bishop).

- Knowledge of basic computer science
principles and skills.

- Familiarity with the basic
probability theory (Stat314 should be sufficient but not
necessary).

- Familiarity with vector calculus
(MTH254 should be sufficient but not necessary) and linear
algebra

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

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

- Lecture 1 Introduction to basic concepts slides

- Lecture 2 Linear regression slides

- Lecture 3 Linear classifier - perceptron

Assignments:

- Reading: review slides posted above
- HW1 , data: training and testing for perceptron (comma separated values, the first column is the class label, +1 for positive class and -1 for negative class); training and testing for regression (comma separated values, the first column is the target variable, i.e., the height, the second one is the knee height, the last one is the armspan.)
- Solution
to HW1

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

*Lecture 4 K-nearest neighbor and model selection slides*

- Lecture 5 Decision trees slides

Assignments:

- Reading: TM's chapter on Decision tree learning, posted on blackboard under course document.
- HW2, data for KNN: training and testing; data for Decision tree: training and testing (HW2 solution)

Assignment:

Assignment:

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

Assignment:

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

- Dimension reduction (and evaluaton of clustering ) slides; matlab scripts for demoing PCA
- Association rule mining slides
- Ensemble methods I slides

**Week 7 (11/11-11/15)
**

- Ensemble methods II slides

- Midterm Sample exam questions; solution set

**Week 8 (11/18- 11/22)
**

Assignment:

**Week 9 (11/25-11/27)
**

- solving MDP cont.
- Reinforcement learning I slides

**Week 10 (12/2 - 12/6)
**

- Reinforcement learning cont. slides