Spring 2007 CAP 5610 Machine Learning: Syllabus

MW 4:30 - 5:45 in BA room 209

Instructor: Dr. Kenneth Stanley

Email: kstanley@cs.ucf.edu
Website: http://www.cs.ucf.edu/~kstanley
EPlex Research Group: http://eplex.cs.ucf.edu
Office: ENG III 332

Office Hours: Monday 2:30-4 Tuesdays 3-5pm


Link to Homework Assignments and Lectures


Machine Learning by Tom M. Mitchell (1997)

Assorted Current Research Papers


This class is a survey of major ideas and methods for giving machines the ability to learn on their own.

There are two main goals in this course:

1. To discuss the field of machine learning including well known machine learning techniques and algorithms, implementation issues, and the types of problems on which different techniques exceed. The aim is to provide both a historical context and practical understanding of the field's most seminal ideas.

2. To experience the academic research process: Students will be asked to read, review, present, and discuss papers from scholarly journals. Students will also be involved in a substantial programming project that must be proposed, implemented, written up as a paper, and presented to the class.

This course will be structured as follows.

1. A major component of this course involves reading papers from journals and conferences relating to the field of machine learning. You will be asked to give one or two presentations to review an assigned paper, discussing the strengths and weaknesses of the paper. You will be graded on your understanding of the paper content, on your review of the paper, and on your presentation of the material.

2. One or two papers will be assigned each week. You must turn in a written review and critique of all papers.

3. Each student will work on an original research project on a related topic of interest to you. You will be asked to write a short proposal of the work you wish to pursue, review background work, implement the project, write a paper describing your efforts and results, and present the paper in class.

4. This is a graduate research oriented course. We want to hear your opinions and comments. Part of your grade will be based on class participation. Participation includes, but is not limited to, class attendance, asking questions, and participation in discussion.

Grading Policy

Grade distribution: Your grade for this class will be determined from the following components:

20% Class participation
20% Written reviews
20% Paper presentations. Presentation Guidelines
40% Final project, paper, and presentation. Proposal Guidelines


All of the work that you turn in or present must be your own. Cheating, plagiarism, and any other form of academic dishonesty will be penalized. The minimum penalty for cheating will include: Plagiarism and paraphrasing are forms of cheating. Plagiarism is the presentation of others' ideas and writings as your own. Paraphrasing is taking someone else's sentence, changing a few words, and then presenting it as your own. Both are unacceptable in this class.


January 8 Introduction

January 10 Intro 2

January 15 Holiday: MLK

January 17 Survey of topics and project planning

January 22 Concept Learning

January 24 Bayesian Learning 1

January 29 Bayesian Learning 2

January 31 Bayesian Learning 3

February 5 Decision Trees 1

February 7 Decision Trees 2

February 12 Learning Theory 1

February 14 Learning Theory 2

February 19 Neural Networks 1

February 21 Neural Networks 2

February 26 Project Proposal Due; Neural Networks 3

February 28 Neural Networks 4

March 5 Reinforcement Learning 1

March 7 Reinforcement Learning 2

March 19 No Free Lunch

March 21 Evolutionary Computation

March 26 Preliminary Project Reports Due; discussion of projects statuses

March 28 Estimation Distribution Algorithm

April 2 Away at CIG. Guest lecture topic: TBD

April 4 Away at CIG. Guest lecture topic: TBD

April 9 Neuroevolution vs. RL

April 11 Ensemble Methods: Boosting

April 16 Recap, special topics.

April 18, 23, 25(final) Projects Due; Project Presentations