The homepage is always under construction. Check the course description and syllabus below to decide if this course suits you.

[March 25th, 2008]: The statistics for midterm is:  High/Low/Median=85/40/60.

[Feb 23rd, 2008]: The statistics for the homework and quizzes are:

• HW 1: High/Low/Median=28/0/20
• Quiz 1: High/Low/Median=10/0/6
• Quiz 2: High/Low/Median=7/0/4
• Quiz 3: High/Low/Median=7/0/2
• Quiz 4: High/Low/Median=10/0/7

Instructor

Dr. Tao Li, Assistant Professor
School of Computer Science and Engineering
Florida International University

Office: ECS 318
Email: taoli AT cs.fiu.edu
Office Hours: Tuesday/Thursday 2:30pm-3:30pm or by appointment

Meeting Time and Location

Tuesday/Thursday 5:00pm-6:15pm, ECS 136

Course Materials

• Lecture Notes and Assignments

Course Description

Machine Learning is concerned with computer programs that automatically improve their performance through experience. This course covers the theory and practice of machine learning from a variety of perspectives.  We cover theoretical concepts, such as inductive bias, the PAC and Mistake-bound learning frameworks, minimum description length principle, and Occam's Razor, and different learning techniques, such as learning decision trees, neural network learning, statistical learning methods, genetic algorithms, Bayesian learning methods, explanation-based learning, and reinforcement learning. The goal of the course is develop a basic understanding on the theory and practice of machine learning from a variety of perspectives and to gain the fundamentals on applying machine learning techniques to real-world applications.

Course Syllabus (Subject to revision)

• Introduction
• Concept Learning and the General-to-Specific Ordering
• Decision Tree Learning
• Artificial Neural Networks
• Evaluating Hypotheses
• Bayesian Learning
• Computational Learning Theory
• Instance-Based Learning
• Genetic Algorithms
• Learning Sets of Rules
• Analytical Learning
• Combining Inductive and Analytical Learning
• Reinforcement Learning

Prerequisites

Graduate Standing. Basically students need to know at least a programming language (e.g., C/C++, Java or Matlab etc.). Students entering the class with basic knowledge of probability, statistics and algorithms will be at an advantage, but the class will be designed so that anyone with basic mathematical background can catch up and fully participate.

Format and Grading

The course assignments include projects, written homeworks, paper discussions and presentations. Research projects will be designed to improve the critical analysis and problem-solving skills of students. Class attendance is mandatory. In addition, occasional quizzes will be given in class. Evaluation will be a subjective process, but it will be primarily based on the students' understanding of the course material. Final grades will be calculated as follows.

Quizzes and Class Participation	15%
Exams	50%
Assignments and Projects	35%

Policies on Assignments and Exams

Textbooks and References

You will not be able to find all the course material in the textbook nor do we plan to go through the chapters in order or in full. You are responsible for the material covered in lectures as well as in the chapters/sections of the text specifically indicated. The following books are useful for the class.

Textbook

• Tom Mitchell. Machine Learning. McGraw Hill, 1997.

Alternative Textbooks

• Christopher M. Bishop. Pattern Recognition and Machine Learning, Springer, 2006
• Ethem Alpaydin. Introduction to Machine Learning, MIT press, 2004.

References

• Hastie, Tibshirani and Friedman. The Elements of Statistical Learning. Springer-Verlag, 2001.

•  R. O. Duda et al., Pattern Classification. Wiley Interscience ,  2001

A lot of reading material from top conferences/journals will be made available online or in class as required. In addition, lecture notes will be available on line.

Code of Academic Integrity:

http://www.fiu.edu/~oabp/misconductweb/2codeofacainteg.htm

University Policies:

• http://www.fiu.edu/provost/polman/sec2/sec2web2-44.htm
• http://www.fiu.edu/~eop/EOPSexH.pdf
• http://www.fiu.edu/~provost/polman/sec19web.html