Syllabus

Instructor: Michael Schneier

Lectures: MWF 10:00-10:50 G36 Benedum Hall

Office Hours: W 1:00pm-2:00pm and by appointment

Office: 600A Thackeray

E-mail: mhs64@pitt.edu

Textbook: There is no assigned textbook for this class. Lectures will be based on a variety of online sources. Recommended references include

• Andrew NG Stanford online machine learning course link (free)
• Convex Optimization – Boyd and Vandenberghe link (free)
• Machine Learning A Probabilistic Perspective - Kevin P Murphy (not free)

Content: This course seeks to provide a broad introduction to the field of machine learning. The emphasis will be the development of a rigorous mathematical basis for the most commonly used algorithms in the field. Topics to be covered include non-convex optimization, convex optimization, clustering, dimensionality reduction and neural networks.

Prerequisites Single variable and multivariable calculus, a knowledge of computing programming, linear algebra. Any programming language can be used in the computational assignments. Assistance will only be provided for Matlab. Matlab can be purchased by students for $5 at https://www.technology.pitt.edu/software/matlab-students

Grading Policy The final grade will 100% be based on homework and projects.

Disability Resource Services

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, 140 William Pitt Union, 412-648-7890 or 412-383-7355 (TTY) as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.

Academic Integrity

Cheating/plagiarism will not be tolerated. Students suspected of violating the University of Pittsburgh Policy on Academic Integrity will incur a minimum sanction of a zero score for the quiz, exam or paper in question. Additional sanctions may be imposed, depending on the severity of the infraction. On homework, you may work with other students or use library resources, but each student must write up his or her solutions independently. Copying solutions from other students will be considered cheating, and handled accordingly.

Statement on Classroom Recording

To address the issue of students recording a lecture or class session, the University's Senate Educational Policy Committee issued the recommended statement on May 4, 2010. ``To ensure the free and open discussion of ideas, students may not record classroom lectures, discussion and/or activities without the advance written permission of the instructor, and any such recording properly approved in advance can be used solely for the student's own private use."