Unsupervised Learning: From Big Data to Low-Dimensional Representations

Overview In the era of data deluge, the development of methods for discovering structure in high-dimensional data is becoming increasingly important. This course will cover state-of-the-art methods from algebraic geometry, sparse and low-rank representations, and statistical learning for modeling and clustering high-dimensional data. The first part of the course will cover methods for modeling data with a single low-dimensional subspace, such as PCA, Robust PCA, Kernel PCA, and manifold learning techniques. The second part of the course will cover methods for modeling data with multiple subspaces, such as algebraic, statistical, sparse and low-rank subspace clustering techniques. The third part of the course will cover applications of these methods in image processing, computer vision, and biomedical imaging.

Beginner 0(0 Ratings) 0 Students enrolled English
Created by Admin corner
Last updated Tue, 07-Jun-2022
+ View more
Course overview
Lecture Details

Unsupervised Learning: Spring 2017 at Johns Hopkins University.
Professor Rene Vidal.

Curriculum for this course
25 Lessons 31:58:27 Hours
Lecture
25 Lessons 31:58:27 Hours
  • Syllabus + Introduction + Basics of Linear Algebra
    Preview 01:21:05
  • Basics of Linear Algebra
    01:15:28
  • Statistical View of PCA
    01:13:12
  • Geometric View of PCA
    01:24:37
  • Rank Minimization View of PCA
    01:16:55
  • Model Selection for PCA
    01:18:08
  • PCA with Missing Entries via Convex Optimization
    01:25:11
  • PCA with Corrupted Entries via Convex Optimization
    01:12:58
  • PCA with Outliers via Convex Optimization L21
    01:11:31
  • Extensions + PCA with Outliers via Convex Optimization L1
    01:21:42
  • Robust PCA via Alternating Minimization I
    01:11:50
  • Robust PCA via Alternating Minimization II
    01:25:15
  • Nonlinear PCA
    01:12:12
  • Kernel PCA
    01:16:39
  • Locally Linear Embedding LLE
    01:11:07
  • Laplacian Eigenmaps LE
    01:19:51
  • LLE + LE
    01:14:43
  • Spectral Clustering
    01:15:55
  • Spectral Clustering + K means
    01:12:45
  • K means and K subspaces
    01:20:11
  • Spectral Subspace Clustering
    01:10:58
  • Local Subspace Affinity & Locally Linear Manifold Clustering
    01:16:13
  • Low Rank Subspace Clustering
    01:21:11
  • Low Rank Subspace Clustering II
    01:08:11
  • Sparse Subspace Clustering
    01:20:39
+ View more
Other related courses
12:40:21 Hours
0 1 Free
29:17:17 Hours
Updated Tue, 07-Jun-2022
0 0 Free
08:06:34 Hours
Updated Tue, 07-Jun-2022
0 0 Free
18:45:19 Hours
0 0 Free
About instructor

Admin corner

0 Reviews | 5 Students | 47 Courses
Student feedback
0
0 Reviews
  • (0)
  • (0)
  • (0)
  • (0)
  • (0)

Reviews

Free
Includes: