**Time: ** Monday, Wednesday
1:00-2:15pm,

** Location: ** Sennott Square, Room 5313

Instructor: **Milos
Hauskrecht**

Computer Science Department

5329 Sennott Square

phone: x4-8845

e-mail: * milos at cs pitt edu*

office hours: Monday 2:30-4:00pm

TA: ** Michael Moeng**

Computer Science Department

5802 Sennot Square

phone: (510) 684-7416

e-mail: * moeng at cs pitt edu *

office hours: Tuesday 11:00-12:00am and 2:00-4:00pm

- Course syllabus
- Matlab tutorial files from January 13, 2010.
- There is no homework assignment due the week of April 12, 2010.
- The final exam will be held on Wednesday, April 21, 2010. The exam is a closed book exam that covers all material not covered by midterm.
- The term project presentations will be held on Monday and Wednesday, the week of April 25, 2010 during the regular class time. The presentation order will be determined by a random drawing.
- The term project reports for the course are due on April 29, 2010 at 11:59pm EST. The final project report should be structured like a conference paper or a journal article. The report should include the introduction, methods, results, discussion and conclusions sections. The key learning methods used in the project should be described to the sufficient depth so that they are selfexplanatory. The clarity of the report will be one of the criterion to grade the project. Here are some examples of projects students submitted in past:
- Solutions to homework assignments:

Course description

Lectures

Homeworks

Term projects

Matlab

The goal of the field of machine learning is to build computer systems that learn from experience and that are capable to adapt to their environments. Learning techniques and methods developed by researchers in this field have been successfully applied to a variety of learning tasks in a broad range of areas, including, for example, text classification, gene discovery, financial forecasting, credit card fraud detection, collaborative filtering, design of adaptive web agents and others.

This introductory machine learning course will give an overview of many models and algorithms used in modern machine learning, including linear models, multi-layer neural networks, support vector machines, density estimation methods, Bayesian belief networks, mixture models, clustering, ensamble methods, and reinforcement learning. The course will give the student the basic ideas and intuition behind these methods, as well as, a more formal understanding of how and why they work. Students will have an opportunity to experiment with machine learning techniques and apply them a selected problem in the context of a term project.

**Prerequisites**

Knowledge of matrices and linear algebra (CS 0280), probability (CS 1151), statistics (CS 1000), programming (CS 1501) or equivalent, or the permission of the instructor.

- Chris Bishop.
*Pattern recognition and Machine Learning*Springer, 2006.

- R.O. Duda, P.E. Hart, D.G. Stork.
*Pattern Classification.*Second edition. John Wiley and Sons, 2000. - C. Bishop.
*Neural networks for Pattern Recognition.*Oxford University Press, 1995. - J. Han, M. Kamber.
*Data mining. Concepts and Techniques. 2nd edition*Morgan Kauffman. - T. Mitchell.
*Machine Learning.*Mc Graw Hill, 1997.

The homework assignments will have mostly a character of projects and will require you to implement some of the learning algorithms covered during lectures. Programming assignmets will be implemented in Matlab. See rules for the submission of programs.

The assignments (both written and programming parts) are ** due at the
beginning of the class on the day specified on the assignment.** In
general, no extensions will be granted.

**Collaborations:**
No collaboration on homework assignments, programs, and exams unless you are specifically
instructed to work in groups, is permitted.

The term project is due at the end of the semester and accounts for a significant portion of your grade. You can choose your own problem topic. You will be asked to write a short proposal for the purpose of approval and feedback. The project must have a distinctive and non-trivial learning or adaptive component. In general, a project may consist of a replication of previously published results, design of new learning methods and their testing, or application of machine learning to a domain or a problem of your interest.

Matlab is a mathematical tool for numerical computation and manipulation, with excellent graphing capabilities. It provides a great deal of support and capabilities for things you will need to run Machine Learning experiments. The CSSD at UPitt offers $5 student licenses for Matlab. To obtain the licence please check the following link to the Matlab CSSD page . In addition, Upitt has a number of Matlab licences running on both unix and windows platforms. See the following web page for the details.

**Other Matlab resources on the web:**

Online
MATLAB documentation

Online
Mathworks documentation including MATLAB toolboxes

** Cheating policy:**
Cheating and any other anti-intellectual behavior, including giving your work to someone else, will be dealt
with severely and will result in the Fail (F) grade. If you feel you may have violated the rules
speak to us as soon as possible. Please make sure you read, understand and abide by
the Academic Integrity Code for the Faculty and College of Arts and Sciences.

** Students With Disabilities:**

If you have a disability for which you are or may be requesting an
accommodation, you are encouraged to contact both your instructor and
Disability Resources and Services, 216 William Pitt Union, (412)
648-7890/(412) 383-7355 (TTY), as early as possible in the term. DRS
will verify your disability and determine reasonable accomodations for
this course.

Course webpages from Spring 2007, Spring 2004 and Spring 2003

Last updated by Milos on 12/31/2009