Instructor:  Milos Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos at cs pitt edu
office hours: Tuesday 2:00-4:00pm

TA:  Chenhai Xi
Computer Science Department
5503 Sennot Square
phone: xxx
e-mail: chenhai at cs pitt edu
office hours: Monday, Thursday 3:00-5:00pm

• Files for Matlab tutorial
• Term projects for the course are due on April 25, 2007. The term project will be evaluated based on:
  • a project report
  • a 10 minute ppt presentation
  The term project presentations will be held on Wednesday, April 25, 2007 from 1:00-4:00pm in SNSQ 5313. The project reports are due (in the electronic form) at the time of your presentation. 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.

  See examples of projects submitted by students in past:

  • Ali Alanjawi. Linear Versus Non -Linear Learning in the Context of Othello.
  • Gregory Khapfhammer. Improving Observation-Based Testing with Database Incorporation Heuristics.
  • Andrew Post. Learning functional clusters of genes in yeast gene expression data.

• Please upload both the reports and your presentations to the course submission dirtectory. I will need to copy your presentations to a laptop, so please upload these by 12:40pm on April 25.

• Solutions for homework assignments:
  • Homework 1 and HW-1 Matlab solutions
  • Homework 2
  • Homework 3 and HW-3 Matlab solutions
  • Homework 4 and HW-4 Matlab solutions
  • Homework 5 and HW-5 Matlab solutions
  • Homework 6
  • Homework 7 and HW-7 Matlab solutions

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.

Course syllabus

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. Morgan Kauffman, 2001.
• 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: You may discuss material with your fellow students, but the report and programs should be written individually.
 

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. Upitt has a number of Matlab licences running on both unix and windows platforms. Click here to find out how to access Matlab at Upitt.

You may want to buy a student license of Matlab for $10 per year. Please see CSSD pages for details.

Matlab tutorial file.

Other Matlab resources on the web:

Online MATLAB  documentation
Online Mathworks documentation including MATLAB toolboxes

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 2004, Spring 2003 and Spring 2002