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CS599 Selected topics in machine learning Schedule Time and Place 12:30pm - 1:50pm Tuesdays and Thursdays, KAP 140Course Descriptions In this seminar-style course, we will investigate a few actively researched and emergent topics in statistical machine learning. The style of the course will focus on studying selected papers from the literature and to work on projects. Topics include: structured prediction, (distance) metric learning, manifold learning and other latent variable modeling techniques, transfer learning/multi-task learning/domain adaptation, compressive sensing and etc. Students are also encouraged to bring research problems from other related domains (vision, speech, natural language processing, robotics, computational biology and etc) into the class.Class Format This is a seminar level course, with reading assignment, presenting papers and working on projects.Grading
Projects Project is a major part of grading and students are encouraged to think about their projects as early as possible. Working as a group is permitted: i) register first with the instructor.; ii) group size is limited to at most 3; iii) quality and contribution of group projects will be shared and accredited by all group members in equal division.
Presentations Depending on enrollment, each student presents papers 2-3 times in class. Formal presentation tools (Powerpoint, Keynote and etc) are strongly preferred. There will be a sign-up sheet at the first class (1/13/09).Prerequisites CS542 or CS567 or CS573, or any other graduate level classes that provided the foundation of machine learning, or permission by instructor.Textbooks There are no required textbooks. Students might find following books useful as general references.
Statement for Students with Disabilities Any student requesting academic accommodations based on a disability is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP. Please be sure the letter is delivered to me (or to TA) as early in the semester as possible. DSP is located in STU 301 and is open 8:30 a.m.-5:00 p.m., Monday through Friday. The phone number for DSP is (213) 740-0776.Statement on Academic Integrity USC seeks to maintain an optimal learning environment. General principles of academic honesty include the concept of respect for the intellectual property of others, the expectation that individual work will be submitted unless otherwise allowed by an instructor, and the obligations both to protect one's own academic work from misuse by others as well as to avoid using another's work as one's own. All students are expected to understand and abide by these principles. Scampus, the Student Guidebook, contains the Student Conduct Code in Section 11.00, while the recommended sanctions are located in Appendix A: http://www.usc.edu/dept/publications/SCAMPUS/gov/. Students will be referred to the Office of Student Judicial Affairs and Community Standards for further review, should there be any suspicion of academic dishonesty. The Review process can be found at: http://www.usc.edu/student-affairs/SJACS/. |
Contact
941 West 37th Place,
Los Angeles, CA 90089
Tel: (213) 740-5924
Fax: (213) 740-7512
Office: RTH 403
Email: feisha@usc.edu
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