Instructor:  Milos Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos at cs pitt edu
office hours: Wednesday: 9:30am-11:00am, Thursday: 3:00pm-4:30pm

TA:  Jeongmin Lee
Computer Science Department
5324 Sennott Square
phone: 4-8455
e-mail: jlee at cs pitt edu
office hours: Tuesday: 2:00pm-5:00pm. Wednesday: 4:00pm-5:00pm, Thursday: TBA

Recitations

• Section 1: Wednesday: 11:00am-11:50am, SENSQ 6110
• Section 2: Wednesday: 3:00pm-3:50pm, SENSQ 6110

• No recitations this week. Please check office hours listed below.
• The final exam for the course will be held on Friday, April 26, 2019 at 8:00-9:50am in SENSQ 5129. The exam is a cumulative, closed-book exam. Please bring your own calculator (no cell phones please).
• Click here to read the guidelines for the final exam and example questions.
• Office hours for the week of April 22-26, 2019:
  • Milos:
    • April 24, Wednesday at 11:00am- 12:30pm
    • April 25, Thursday at 3:00pm-4:30pm
  • Jeongmin
    • April 23, Tuesday at 2:00-5:00pm
    • April 24, Wednesday at 9:30am-11:00am, 3:00pm-5:00pm
    • April 25, Thursday 1:00-3:00pm
• Course syllabus

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 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, and 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.

Course syllabus

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.

• Hal Daume III. A Course in Machine Learning.

• 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 consist of a mix of theoretical and programming problems. Programming part will require you to implement in Matlab some of the learning algorithms covered during lectures. See rules for the submission of programs.

The assignments (both theoretical 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: 45%
• Midterm: 20%
• Final exam: 25%
• Lectures (attendance/activity): 10%

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 University of Pittsburgh and School of Computing and Information (SCI).

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.