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Course Offerings

Course Details

Spring 2013-2014
ELE 571  

Digital Neurocomputing

Sun-Yuan Kung

Machine learning algorithms allow us to induce rules based on empirical training data. The course establishes statistical and algebraic foundations essential to kernel-based learning approach, for vectorial and nonvectorial data analysis. More specifically, it covers: (i) aptive techniques for feature selection and dimension reduction PCA, SODA and DCA; (ii) unsupervised cluster discovery: K-means, SOM, kernel K-means, kernel SOM, and hierarchical clustering; and (iii) supervised learning algorithms: Fisher discriminate analysis (FDA), kernel ridge regressor (KRR), support vector machines (SVM), and ridge-SVM.

Sample reading list:
S.Y. Kung, Kernal Methods and Machine Learning (Cambridge Press, 2014)

Other Requirements:
Not Open to Freshmen.

Other information:
The grade will be based on course projects.

Website:  http://www.blackboard.princeton.edu

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
42693 S01 9:30 am - 10:50 am M W   Engineering Quad A-Wing   A224   Enrolled:10 Limit:16