CS2750  Machine Learning (ISSP 2170)

Time:  Tuesday, Thursday 1:00pm-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 2:30-4:00PM, Wednesday 10:00-11:30am

TA:  Yanbing Xue
Computer Science Department
5324 Sennott Square
e-mail: yax14 at pitt edu
office hours: Monday 2:00-3:30pm, Wednesday 3:30-5:00pm

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 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 solutions on various datasets and apply them a selected problem in the context of a term project.

Course syllabus


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


Other ML books:

Lectures  Topic(s)  Assignments
January 9 Introduction to Machine Learning.

Readings: Bishop: Chapter 1

January 11 Designing a learning system.

Readings: Bishop: Chapter 1

January 16 Matlab tutorial.


January 18 Designing a learning system

Readings: Bishop: Chapter 1

Homework assignment 1 ( Data for HW-1)
January 23 Designing a learning system (cont)
Density estimation (introduction)

Readings: Bishop: Chapter 1,

January 25 Density estimation

Readings: Bishop: Chapter 2.1-2

Homework assignment 2 ( Data for HW-2)
January 30 Density estimation II

Readings: Bishop: Chapter 2.2-3.

February 1 Density estimation III

Readings: Bishop: Chapter 2.4-5

Homework assignment 3 ( Data for HW-3)
February 5 Linear regression

Readings: Bishop: Chapter 3.1-2.

February 8 Linear regression II
Linear models for classification

Readings: Bishop: Chapter 4.1. and 4.3.

Homework assignment 4 ( Data for HW-4)
February 13 Generative classification models

Readings: Bishop: Chapter 4.2.

February 15 Evaluation of classifiers
Support vector machines

Readings: Bishop: Chapter 7.1.

Homework assignment 5 ( Data for HW-5)
February 20 Support vector machines II
Decision Trees

Readings: Bishop: Chapters 7.1., 14.4.

February 22 Multilayer neural networks

Readings: Bishop: Chapters 5.1-3, 5.5.

Homework assignment 6 ( Data for HW-6)
March 1 Bayesian belief networks

Readings: Bishop: Chapters 8.1-2

March 13 Bayesian belief networks II

Readings: Bishop: Chapters 8.1-2

March 15 Bayesian belief networks: inference

Readings: Bishop: Chapters 8.1-2, 8.4.

Homework assignment 7 ( Data for HW-7)
March 20 Expectation Maximization

Readings: Bishop: Chapter 9.3-4, 9.2.

March 22 Clustering

Readings: (no readings in Bishop) you may want to read Clustering chapter in:
Han, Kamber, and Pei. Data mining: Concepts and Techniques, 2012.

Homework assignment 8 ( Data for HW-8)
March 24 Python tools for ML


March 29 Clustering


Homework assignment 9 ( Data for HW-9)
April 3 Learning with multiple models: Mixture of experts. Bagging.


  • Chapter 14 and Section 4.3.4 (multiclass logreg)
April 5 Learning with multiple models: Boosting.
Reinforcement learning I.


Homework assignment 10 ( Data for HW-10)
April 10 Reinforcement learning II.



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

The assignment reports and programs should be submitted electronically via Course web. The assignments 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 is permitted unless you are specifically instructed to work in groups.

Term projects

The term project is due at the end of the semester and accounts for a significant portion of your grade.


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.

Matlab tutorial file.

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.

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 as early as possible in the term. DRS will verify your disability and determine reasonable accomodations for this course.

Course webpage from Spring 2015

Last updated by Milos on 01/09/2018