**Time: ** Tuesday, Thursday
11:00pm-12:15pm

** Location: ** Sennott Square (SENSQ), Room 5502

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 10:30am-noon

TA: ** Yanbing Xue **

Computer Science Department

5324 Sennot Square

phone: 4-8455

e-mail: * yax14 at pitt edu *

office hours: Tuesday 4:00-5:00pm, Wednesday 1:00-3:00pm

- Course syllabus
- The final exam for the class is scheduled for Thursday, December 14, 2017 at 8:00-9:50am in SENSQ 5502, the same room the class met during the semester. The final exam isL closed-book and cumulative (covers the material from the whole semester, except the lecture on ML tools from December 6, 2017).

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.

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

- 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.

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

** Grading:**
Your grade for the course will be determined as follows:

- Homework assignments: 50%
- Midterm: 20%
- Final exam: 25%
- Lectures (attendance/activity): 5%

** 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.

Last updated by Milos on 08/28/2017