CS2750  Machine Learning (ISSP 2170)

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: 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

Announcements !!!!!


Course description
Term projects


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


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.


Other very useful books:

Lectures  Topic(s)  Assignments
January 3 Introduction.


January 8 Overfit. Designing a learning system

Readings : Bishop. Chapter 1. Section 1.1.

January 10 Density estimation

Readings : Bishop. Review of probability (Chapter 1.2) & Chapter 2.1.

January 17 Matlab tutorial Homework 1
Data for the assignment
January 22 Density estimation II.

Readings : Bishop. Chapter 2.1.

January 24 Density estimation III.

Readings : Bishop. Chapter 2.

Homework 2
Data for the assignment
January 24 Exponential family. Linear regression.

Readings : Bishop. Chapter 2, and Chapter 3.1.

January 31 Linear regression. Statistical view. Regularization.

Readings : Bishop. Chapter 3.

Homework 3
Data for the assignment
February 5 Logistic regression.

Readings : Bishop. Chapter 4.

February 7 Generative classification models. GLMs.

Readings : Bishop. Chapter 4.

Homework 4
Data for the assignment
February 12 Multiway classification. Softmax

Readings : Bishop. Chapter 4.

February 14 Evaluation of classifiers. MLPs.

Readings : Bishop. Chapter 5.

Homework 5
Data for the assignment
February 19 Multi-layer Neural Networks.

Readings : Bishop. Chapter 5.

February 21 Support vector machines.

Readings : Bishop. Chapter 7.

February 26 Support vector machines for regression.

Readings : Bishop. Chapter 7.

February 28 Midterm exam
March 12 Bayesian belief networks.

Readings : Bishop. Chapter 8.1. and 8.2.

March 14 Bayesian belief networks II.

Readings : Bishop. Chapter 8.1. and 8.2.

Homework 6
Data and programs for the assignment
March 19 Bayesian belief networks. Inference and Learning

Readings : Bishop. Chapter 8, Chapter 11.1-2.

March 21 Bayesian belief networks. Learning.

Readings : Bishop. Chapter 8.

Homework assignment 7
Data and programs for the assignment
March 26 Learning BBNs with hidden variables and missing values. EM.

Readings : Bishop. Chapter 9.

March 28 Learning BBNs with hidden variables and missing values.

Readings : Bishop. Chapter 9.

Homework assignment 8
Data and programs for the assignment
April 2 Clustering

Readings : lecture notes.

April 4 Dimensionality reduction. Feature selection.

Readings : Chapter 12.1.

April 9 Decision trees. Mixture of experts.

Readings : Chapter 14.4-5.,

April 11 Ensambles methods. Mixture of experts. Bagging.

Readings : Chapter 14.2. & 14.4.,

April 16 Ensambles methods. Boosting.

Readings : Chapter 14.2. & 14.4.

April 25 Project presentations


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.

Term projects

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

Last updated by Milos on 12/23/2006