Time: Monday, Wednesday
1:00-2:15pm,
Location: Sennott Square, Room 5129
Instructor: Milos
Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos at cs pitt edu
office hours: Thursday 1:00-3:00pm
TA: Zitao Liu
Computer Science Department
5324 Sennot Square
phone:
e-mail: ztliu at cs pitt edu
office hours: Tuesday 1:30-4:30pm
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.
Lectures | Topic(s) | Assignments | |
---|---|---|---|
January 4 |
Introduction to Machine Learning.
Readings: Bishop: Chapter 1 |
. | |
January 9 |
Machine Learning: designing a learning system
Readings: Bishop: Chapter 1 |
. | |
January 11 |
Matlab tutorial
Readings: Bishop: Chapter 1 |
. | |
January 18 |
Matlab tutorial II
Readings: Bishop: Chapter 1 |
Homework 1 ( Data ) | |
January 23 |
Density estimation I
Readings: Bishop: Chapter 2.1. |
. | |
January 25 |
Density estimation II
Readings: Bishop: Chapter 2.2-3 |
Homework 2 ( Data ) | |
January 30 |
Density estimation III
Readings: Bishop: Chapter 2 |
. | |
February 1 |
Linear regression
Readings: Bishop: Chapter 3.1-2. (optional 3.3-3.5) |
Homework 3 ( Data ) | |
February 6 |
Classification I. Logistic regression. Generative classification models.
Readings: Bishop: Chapter 4.2-3. |
. | |
February 8 |
Classification II. Naive Bayes model. Evaluation of classification performance.
Readings: Bishop: Chapter 4 |
Homework 4 ( Data ) | |
February 13 |
Support vector machines
Readings: Bishop: Chapter 4.1.2-4, Chapter 7. |
. | |
February 15 |
Support vector regression. Multilayer Neural networks
Readings: Bishop: Chapters 7, 5 |
Homework 5 ( Data ) | |
February 20 |
Nonparametric density estimation and classification methods
Readings: Bishop: Chapter 2.5. |
. | |
February 22 |
Classification: Decision trees Bayesian belief networks Readings: Bishop: Chapters 8.1-2 |
Homework 6 ( Data ) | |
February 27 |
Bayesian belief networks
Readings: Bishop: Chapter 8.1.-8.2. |
. | |
February 29 |
Bayesian belief networks: inference and learning
Readings: Bishop: Chapter 8.2, 8.4. |
. | |
March 5-9 | Spring break | . | |
March 12 |
Learning structure of Bayesian belief networks
Readings: Bishop: Chapter 8.4. |
. | |
March 14 | Midterm exam | Homework 7 ( Data ) | |
March 19 |
Learning with hidden variables and missing values: EM algorithm.
Readings: Bishop: Chapter 8.4. |
. | |
March 21 | Clustering I. | Homework 8 ( Data ) | |
March 26 |
Clustering.
Readings: Bishop: Chapter 9. |
. | |
March 28 |
Feature selection, Dimensionality reduction.
Readings: Bishop: Chapter 12.1. |
Homework 9 ( Data ) | |
April 2 |
Ensamble methods: mixture of experts. Bagging.
Readings: Bishop: Chapter 14.
|
. | |
April 4 |
Ensamble methods: boosting.
Readings: Bishop: Chapter |
Homework 10 ( Data ) | |
April 9 |
Reinforcement Learning
Readings: Kaelbling, Littman, Moore. Reinforcement Learning: a survey |
. | |
April 11 |
Reinforcement Learning II
Readings: Kaelbling, Littman, Moore. Reinforcement Learning: a survey |
. |
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.
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. See the following web
page for the details.
Other Matlab resources on the web:
Online
MATLAB documentation
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: Course webpages from Spring 2011, Spring 2010, Spring
2004 and Spring 2003 .
Online
Mathworks documentation including MATLAB toolboxes
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.
Last updated by Milos
on 12/31/2011