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

• 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).

---

Prerequisites:

Students are expected to have the following background:

• Knowledge of basic computer science principles and skills.

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

• Slides for review of basic probability concepts

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

• Slides for review of basic concepts in vector calculus and linear algebra

---

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)

• 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
  

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

• Lecture 6 Bayes rule slides, bayes classifiers slides

    Assignment:

• HW3 , written assignment only.
  

---