CS 479 / CS 579 Section 1 Course Syllabus Spring 2005
Machine Learning

Textbook: Ethem Alpaydin, Introduction to Machine Learning, The MIT Press, October 2004, ISBN 0-262-01211-1.

Prerequisites: It is expected that students taking the course have good programming skills and a rudimentary knowledge of probability, statistics and linear algebra.

Students are responsible for all lecture material, handouts and announcements given during class.

Course Content: Learning -- ie, using experience to improve performance -- is an essential component of intelligence. The field of Machine Learning, which addresses the challenge of producing machines that can learn, has become an extremely active, and exciting area, with an ever expanding inventory of practical (and profitable!) results, many enabled by recent advances in the underlying theory.

This course provides an introduction to machine learning. It will cover practical aspects as well as theoretical concepts of the field. Practical aspects covered in the course will include the top five algorithms: learning decision trees, neural networks, nearest neighbor, support vector machines, discrete naive bayes. Theoretical concepts presented in the course will include Bayesian learning and the PAC learning framework.

At the end of the course we will move to student presentations based on recent journal and conference papers. Each student will make a presentation.

Presentations: In-class presentations will use either transparencies or a computer. Quality of presentation will be graded as well as content. You should choose material of technical interest, demonstrate your understanding of the material, and leave your audience with an understanding of most of the material presented.

Student Evaluation:

Homework	55%
Presentations	25%
Quizzes	12%
Class participation	8%

Grading Scale:

90-100% = A	80-89.99 = B	70-79.99 = C
60-69.99 = D	below 60 = F

Homework assignments will include paper-and-pencil as well as programming assignments.

You may be asked to read the material before class. This will allow to use class time more efficiently to concentrate on the hardest points and to concentrate on the applications. The purpose of the quizzes will be mostly to assess readings.

Due Dates and Late Policy: Every assignment will have a due date. Every school day the assignment is late, the penalty is 5 percent of the possible points off. Even an assignment turned in after the assignments have been collected in class the day it was due it will lose 5 percent. Late assignments will not be accepted once the graded work has been returned to the class.

Class Policy If you decide to withdraw from the course, you are responsible for ensuring that all steps are taken to formally withdraw. Do not assume that you will be dropped automatically.

The grade of I (incomplete) may be given only if you are unable to complete the course due to documented circumstances beyond your control that develop after the last day to withdraw from the course. Appropriate circumstances include illness and death or crisis in your immediate family. Consult the university catalog for regulations regarding the I grade. In no case will an I grade be assigned to avoid a grade of D or F in the course. If you elect to be graded under the S/U option, you must declare your intention when registering for the course. All work in the class will be graded in a manner identical to that for students choosing the letter grade option. At the end of the semester, your final letter grade in the course will be used to assign either a S or an U. You must achieve a minimum grade of C in order to receive a grade of S.

It is expected that students follow the code of conduct stated in the University Student Handbook. Any violations of the code will result in a grade of F for the course, in addition to any further sanctions imposed by the university. Unless explicitly stated by the instructor, you are assumed to perform the assigned work by yourself, without any external collaboration. Cheating in all forms is prohibited. Note that a person copying an assignment is guilty of a violation of academic conduct, as is the person from whom the assignment was copied.

If you have or believe you have a disability and would benefit from accommodations, you may wish to self-identify. You can do so by providing documentation to the Office for Services for Students with Disabilities (SSD), located at Garcia Annex Room 102 (phone: 646-6840, TTY 646-1918). If you are already registered with the SSD Office and need accommodations please provide your "Accommodations Memo" from the SSD Office within the first two weeks of class. If you have a condition which may affect your ability to exit safely from the premises in an emergency or which may cause an emergency during class, you are encouraged to discuss this in confidence with the instructor and/or the Coordinator for SSD. Feel free to call Ms. Elva G. Telles, EEO/ADA & Employee Relations Director at 646-3333 with any questions about the Americans with Disabilities Act (ADA) and/or Section 504 of the Rehabilitation Act of 1973. All medical information will be held in strict confidence.