WCOMS4774-1: Unsupervised Learning for Fall 2022

Days and Time

Tuesdays and Thursdays 1:10 PM-2:25 PM

Location

RTBA BTBA

Allowed For:

  • Undergraduate
  • Masters
  • Professional
  • PhD

Prerequisites:

None

Notes:

None

Instructor:

Verma, Nakul

Description

Core topics from unsupervised learning such as clustering, dimensionality reduction and density estimation will be studied in detail. Topics in clustering: k-means clustering, hierarchical clustering, spectral clustering, clustering with various forms of feedback, good initialization techniques and convergence analysis of various clustering procedures. Topics in dimensionality reduction: linear techniques such as PCA, ICA, Factor Analysis, Random Projections, non-linear techniques such as LLE, IsoMap, Laplacian Eigenmaps, tSNE, and study of embeddings of general metric spaces, what sorts of theoretical guarantees can one provide about such techniques. Miscellaneous topics: design and analysis of datastructures for fast Nearest Neighbor search such as Cover Trees and LSH. Algorithms will be implemented in either Matlab or Python.