CS 2750 Machine Learning

CS1675 Introduction to Machine Learning
Time: TH 4:00 - 5:15pm
Location: Sennott Square, Room 5129

Instructor: Milos Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos at pitt edu
Office hours: Monday 1:00-2:30pm, Tuesday 10:30 - noon

TA: Amin Sobhani
Computer Science Department
6804 Sennott Square
phone: x4-8456
e-mail: ams543 at pitt edu
office hours: Tuesday, Wednesday: 2:30-4:00pm

Announcements !!!!!

Links
Course description
Lectures
Homeworks
Term projects
Matlab

Abstract
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, probability (CS 1151) and statistics (CS 1000), programming (CS 1501) or equivalent, or the permission of the instructor.
Course syllabus

Textbook:

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

Other ML books:

K. Murphy. Machine Learning: A probabilistic perspective, MIT Press, 2012.
R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification. Second edition. John Wiley and Sons, 2000.
J. Han, M. Kamber. Data mining. Concepts and Techniques. Morgan Kauffman.
T. Mitchell. Machine Learning. Mc Graw Hill, 1997.

Lectures

Lectures	Topic(s)	Assignments
January 5	Introduction to Machine Learning. Readings: Bishop: Chapter 1	.
January 7	Introduction to Machine Learning. Readings: Bishop: Chapter 1	.
January 12	Density Estimation I. Readings: Bishop. Chapter 2.1-3.	.
January 14	Matlab tutorial Readings:	Homework assignment 1 ( Data for homework 1)
January 21	Density estimation II Readings: Bishop. Chapter 2.1-3.	Homework assignment 2 ( Data for homework 2)
January 26	Density estimation III Readings: Bishop. Chapter 2.	.
January 28	Linear regression Readings: Bishop. Chapter 3.1.	Homework assignment 3 ( Data for homework 3)
February 2	Classification learning: Logistic Regression, Generative classification models. Readings: Bishop. Chapter 4.2-3.	.
February 4	Classification learning II. Evaluation of classifiers. Readings: Bishop. Chapter 4.	Homework assignment 4 ( Data for homework 4)
February 9	Fisher Linear Discriminant. Support Vector Machines. Readings: Bishop: Chapter 4.1.2-4, Chapter 7.	.
February 11	Support vector machines for regression. Nonparametric/instance based classification methods Readings: Bishop. Chapter 4.	Homework assignment 5 ( Data for homework 5)
February 16	Multilayer Neural Networks Readings: Bishop. Chapter 5.	.
February 18	Multiclass classification. Decision trees. Readings: Bishop. Chapter .	Homework assignment 6 ( Data for homework 6)
February 23	Bayesian Belief Networks Readings: Bishop. Chapter .	.
February 25	Bayesian Belief Networks. Inference and Learning. Readings: Bishop. Chapter .	Homework assignment 7 ( Data for homework 7)
March 2	Midterm exam Readings: Everything covered before or on February 25, 2015.	.
March 4	Expectation maximization algorithm. Readings: Bishop. Chapter 8.	Homework assignment 8 ( Data for homework 8)
March 16	Expectation maximization algorithm. Mixture of Gaussians. Readings: Bishop. Chapter 9.	.
March 18	Clustering. Readings: Bishop. Chapter 9.	Homework assignment 9 ( Data for homework 9)
March 23	Ensemble methods: Mixture of experts. Bagging. Readings: Bishop: Chapter 14. L. Breiman. Arcing classifiers Y. Freund, R. Schapire. Experiments with a new boosting algorithm	.
March 25	Ensemble methods: Boosting. Readings: Bishop: Chapter 14. L. Breiman. Arcing classifiers Y. Freund, R. Schapire. Experiments with a new boosting algorithm	Homework assignment 10 ( Data for homework 10)
March 30	Dimensionality reduction. Feature selection. Readings: Bishop: Chapter 12.1, 12.4.	.
April 1	Dimensionality reduction II. Reinforcement learning. Readings: Bishop: Chapter 12.1, 12.4. Kaebling, Littman, Moore. Reinforcement Learning: a survey	.
April 6	Reinforcement learning Readings: Kaebling, Littman, Moore. Reinforcement Learning: a survey	.
April 8	Concept learning Readings:	.
April 15	Final exam Readings: all semester	.
April 20 and 22	Term project presentations Readings: all semester	.

Homeworks 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 assignments 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 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 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 free student licenses for Matlab. To obtain the licence please check the following link to the Matlab CSSD page . In addition, Matlab is available for use in the university computing labs. 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 as early as possible in the term. DRS will verify your disability and determine reasonable accomodations for this course.

Last updated by Milos on 01/04/2017