Time: Tuesday, Thursday
4:00pm-5:15pm
Location: Sennott Square (SENSQ), Room 5129
Instructor: Milos
Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos at cs pitt edu
office hours: Tuesday 1:00-2:30pm, Wednesday 11:00am-12:30pm
TA: Amin Sobhani
Computer Science Department
6804 Sennot Square
phone: 412-624-8456
e-mail: ams543 at pitt edu
office hours: Tuesday, Wednesday: 2:30-4:00pm
Course description
Lectures
Homeworks
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, clustering, ensemble methods, and reinforcement of 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. Through homework assignments students will have an opportunity to experiment with many machine learning techniques and apply them to various real-world datasets.
Prerequisites
STAT 1000, 1100, or 1151 (or equivalent), and CS 1501, or the permission of the instructor.
Lectures | Topic(s) | Assignments | |
---|---|---|---|
January 5 |
Introduction to Machine Learning.
Readings: Bishop: Chapter 1 |
. | |
January 10 | Matlab tutorial | . | |
January 12 |
Introduction to Machine Learning II.
Readings: Bishop: Chapter 1 |
Homework assignment 1, Data | |
January 17 |
Introduction to Machine Learning III.
Readings: Bishop: Chapter 1 |
||
January 19 |
Evaluation of ML algorithms. Density estimation Readings: Bishop: Chapter 1,Chapter 2 |
Homework assignment 2, Data | |
January 24 |
Density estimation
Readings: Bishop: Chapter 2 |
||
January 26 |
Density estimation
Readings: Bishop: Chapter 2 |
Homework assignment 3, Data | |
January 31 |
Linear regression
Readings: Bishop: Chapter 3 |
||
February 2 |
Logistic regression
Readings: Bishop: Chapter 4.1.; 4.3. |
Homework assignment 4, Data | |
February 7 |
Generative classification models
Readings: Bishop: Chapter 4.3, 4.2. |
||
February 9 |
Support vector machines
Readings: Bishop: Chapter 7.1. |
Homework assignment 5, Data | |
February 14 |
Support vector machines (cont), ROC analysis, and nonparametric classifiers
Readings: Bishop: Chapters: 2.5., 7.1. |
||
February 16 |
Multilayer neural networks
Readings: Bishop: Chapter 5.1.-5.3. |
Homework assignment 6 , Data, Programs | |
February 21 |
Decision trees Bayesian belief networks Readings: Bishop: Chapter 14.4. and 8.1. |
||
February 23 |
Bayesian belief networks
Readings: Bishop: Chapter 8.1-2. |
Homework assignment 7 , Data, Programs | |
February 28 |
Bayesian belief networks (learning and inference)
Readings: Bishop: Chapter 8.1-2. |
||
March 2 | Midterm | ||
March 14 | Midterm solutions | ||
March 16 |
Bayesian belief networks: inference
Readings: Bishop: Chapter 8.1-2. |
Homework assignment 8 , Data | |
March 21 |
Modeling complex probability distributions. Clustering
Readings: Bishop: |
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.
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. The info about how to get a licence please check the following link to the Matlab CSSD page . Note that in addition, Matlab is available for use in the university computing labs. See the CSSD 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. 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 University of Pittsburgh and 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 (DRS),
140 William Pitt Union, (412) 648-7890, drsrecep@pitt.edu, (412) 228-5347 for P3 ASL users, as early as possible in the term.
DRS will verify your disability and determine reasonable accommodations for this course.