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Course Evaluation Results

Course Details

Fall 2016-2017
ELE 477   Graded A-F, P/D/F, Audit

Kernel-Based Machine Learning

Sun-Yuan Kung

With foundation built upon statistical and algebraic learning theory, this course offers an in-depth learning experience on machine learning for (big) data analysis for senior and graduate students in electrical engineering, computer science, and applied statistics - with some exposure to algebra and statistics. It covers various kernel-based unsupervised and supervised learning models and provides an integrated understanding of the mathematical theory and their potential applications. With the accompanied software learning laboratories. It also demonstrates how kernel learning models work for pattern recognition and data analysis.

Sample reading list:
Kung, S.Y, Kernel Method and Machine Learning

Reading/Writing assignments:
20 pages of reading per week.

Requirements/Grading:
Mid Term Exam - 25%
Final Exam - 35%
Lab Reports - 15%
Problem set(s) - 25%

Other Requirements:
Statistical, design or other software use required
Open to Juniors, Seniors, and Graduate Students Only.

Prerequisites and Restrictions:
Some prior exposure to linear algebra, statistical theory, and convex optimization..

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
22708 L01 3:00 pm - 4:20 pm M W   Friend Center   003   Enrolled:9 Limit:25