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
phone:
e-mail: yax14 at pitt edu
office hours: Monday 2:00-3:30pm, Wednesday 3:30-5:00pm

• Course syllabus
• The term project reports are due on April 26, 2018 at 1:00pm. The project report should be organized like a conference paper with abstract, introduction, related work, methodology, experimental results, discussion of results and conclusion sections. The reports should be self-explanatory. The methodology section should explain briefly but clearly the methods used and provide relevant references to past work. The code you have developed (not the data !!!) should be submitted in the zip file via a separate link Term project submission link. Please acknowledge any code/packages used to develop the models or the ML algorithms.
• The term project presentations will be held on April 24, 2018 and April 26, 2018 at 1:00pm during the regularly scheduled class time. Click here to access the order of presentations and presentation guidelines . The presentations in the pdf format should be submitted via Course Web by 12:30pm on the day the presentation is scheduled.
• The TA office hours are now held in 5324 Sennott Square.
• Here are brief reviews of Basic Math for ML and Basics of probabilities for ML .

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 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

Prerequisites

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

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