Time and Place:
Monday 16:15-18:45 in HNB 107
In a seminar style, this course will discuss the most recent developments in statistical learning by studying selected papers from the literature. Topics include Bayesian statistics, Bayes nets and graphical models, sequential estimation techniques, Monte Carlo methods, developments in support vector machines, advanced methods of nonlinear dimensionality reduction, automatic structure selection in learning, bootstrapping and boosting methods, ensemble learning, and others. We will study these topics in applications of computer vision, robotics, human computer interaction (HCI), imitation learning, real-time learning, and data mining.
This course follows an informal seminar style, focussing presentations and participation of the participants.
The reading list is on the syllabus page. Additionally, the following books are excellent background readings:
- Neural Networks for Pattern Recognition (Bishop, C.M., 1995, Oxford University Press)
- Information Theory, Inference, and Learning Algorithms (MacKay, D.J.C., 2003, Cambridge University Press). This books has an online version.
- 25% active participation in class
- 75% \"paper presentations\" and/or \"projects\" and/or \"solving statistical data problems\"
CS 542, CS 567, or any other graduate level machine learning.
Dr. Stefan Schaal
University of Southern California
Hedco Neurosciences Building HNB-103
Los Angeles, CA 90089-2520
phone: (213) 740 9418
According to email arrangement with instructor.
All students are required to abide by the USC code of Academic Integrity. Violation of that Code will be dealt with as described in SCAMPUS. If you have any questions about the responsibilities of either students, faculty, or graders under this policy, contact the instructor or the Office of Student Conduct.
Disabilities and Academic Accomodations:
Students requesting academic accomodations based on a disability are required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accomodations can be obtained from DSP when adequate documentaion is filed. Please be sure the letter is delivered to the instructor (or TA) as early in the semester as possible. DSP is open Monday-Friday, 8:30-5:00. The office is in Student Union 301 and their phone number is (213) 740-0776.