Dimensionality Reduction - Spring 2010 - Info
*** THURSDAYS 1:30-3:20PM (719 Broadway Bldg, 12th floor) ***
Course info sheet.
Instructor: Sam Roweis
Please do NOT send instructors or tutors email about the class directly to their personal accounts.
Lecture Times: Thursdays 1:30-3:20pm
Office hours: Thursdays 3:30-4:30pm or by appointment, Broadway 715, room 1206.
Prerequisite: none for NYU grad students, instructor permission otherwise;
This course will review computational methods for reducing the dimensionality of high dimensional data which lie on or near a manifold of low intrinsic dimensionality. Topics will include: linear methods (such as principal components analysis, factor analysis, singular value decomposition); classic visualizations methods (such as multidimensional scaling and its non-metric variants); and more recent methods based on eigenvectors of Laplacians and convex optimization (such as Kernel PCA, Locally Linear Embedding, Isomap and Maximum Variance Unfolding). Both theoretical and algorithmic properties of the methods will be discussed. Coursework will include small scale computational experiments and readings of primary source research papers.