CSE 291D. Unsupervised Learning

Spring 2011
Professor: Lawrence Saul
Lectures: Tue/Thu 11:00 am  12:20 pm
Location: CogSci Building, Room 4
Office hours: after class and/or by appointment.
Units: 4
The lectures in this course will survey leading algorithms for unsupervised learning and high dimensional data analysis. The first part of the course will cover probabilistic/generative models of high dimensional data, such as Gaussian mixture models, factor analysis, nonnegative matrix factorization, exponential family PCA, probabilistic latent semantic analysis, latent Dirichlet allocation, independent component analysis, and deep neural networks. The second part of the course will cover spectral methods for dimensionality reduction, including multidimensional scaling, Isomap, maximum variance unfolding, locally linear embedding, graph Laplacian methods, spectral clustering, and kernel PCA.
The course is aimed at graduate
students in machine learning and related fields. Students should have earned a high grade in a previous, related course, such as CSE 250A, CSE 250B, ECE 271A, or ECE 271B. The course will be taught by lecture in the same style as CSE 250A, though at a more advanced mathematical level.
Enrollment is by permission of the instructor.
There will be three homework assignments (6075%) and a final course project (2540%). Students may also be required to submit "scribe notes" (handwritten or typeset) on a small subset of lectures.
Tue Mar 29 
Course overview. Review of clustering: kmeans algorithm, Gaussian mixture modeling. 
Thu Mar 31 
Review of linear dimensionality reduction: principal component analysis, factor analysis. 
Tue Apr 05 
EM algorithms for factor analysis, principal component analysis, and mixture of factor analyzers. 
Thu Apr 07 
Nonnegative matrix factorization: cost functions and multiplicative updates. 
Tue Apr 09 
Nonnegative matrix factorization: auxiliary functions and proofs of convergence. 
Thu Apr 14 
Exponential family PCA. 
Tue Apr 19 
Singular value decomposition, lowrank matrix approximations, multidimensional scaling.

Thu Apr 21 
Manifold learning, Isomap algorithm.

Tue Apr 26 
Nystrom approximation; maximum variance unfolding (MVU).

Thu Apr 28 
Spectral clustering, normalized cuts, graph partitioning.

Tue May 03 
Laplacian eigenmaps, locally linear embedding (LLE).

Thu May 05 
Low rank factorizations for MVU, kernel PCA, class evaluations.

Tue May 10 
Document modeling: bagofwords representation, probabilistic latent semantic indexing, Dirichlet models. 
Thu May 12 
Latent Dirichlet allocation. 
Tue May 17 
Variational approximations for inference. 
Thu May 19 
Independent component analysis: maximum likelihood, contrast functions. 
Tue May 24 
Fixed point methods; blind source separation. 
Thu May 26 
Student presentations: Andrew Gross, Edward O'Brien and Chris DeBoever, Rohan Anil, Moahammed Saberian.

Tue May 31 
Student presentations: Vineet Kumar, Baris Aksanli, Samyeul Noh and Sunghee Woo, Katherine Ellis, Daryl Lim, He Huang.

Thu Jun 02 
Student presentations: Matt Der, Vivek Ramavajjala, Akshay Balsubramani, Ashish Venkat, Elkin Dario Gutierrez.

Probabilistic PCA
Nonnegative matrix factorization
Exponential family PCA

M. Collins, S. Dasgupta, and R. E. Schapire (2002).
A generalization of principal component analysis to the exponential family. In T. G. Dietterich, S. Becker, and Z. Ghahramani (eds.), Advances in Neural Information Processing Systems 13, pages 617624. MIT Press: Cambridge, MA.

I. Rish, G. Grabarnik, G. Cecchi, F. Pereira, and G. J. Gordon (2008).
Closedform supervised dimensionality reduction with generalized linear models.
In Proceedings of the Twenty Fifth International Conference on Machine Learning (ICML08), pages 832839. Helsinki, Finland.
Document modeling
Independent component analysis
Deep architectures
Multidimensional scaling and Nystrom approximation
Isomap and extensions
Maximum variance unfolding
K. Q. Weinberger and L. K. Saul (2006).
An introduction to nonlinear dimensionality reduction by maximum variance unfolding.
In Proceedings of the Twenty First National Conference on
Artificial Intelligence (AAAI06), pages 16831686. Boston, MA.
J. Sun, S. Boyd, L. Xiao, and P. Diaconis (2006).
The fastest mixing Markov process on a graph and a connection to a maximum variance unfolding problem. SIAM Review 48(4):681699.
K. Q. Weinberger, F. Sha, Q. Zhu, and L. K. Saul (2007).
Graph Laplacian regularization for largescale semidefinite programming.
In B. Schoelkopf, J. Platt, and T. Hofmann (eds.), Advances in Neural Information Processing Systems 19, pages 14891496. MIT Press: Cambridge, MA.
Spectral clustering
Graph Laplacian methods
Locally linear embedding and related work
Kernel PCA
B. Schoelkopf, A. J. Smola, K. R. Mueller (1998).
Nonlinear component analysis as a kernel eigenvalue problem.
Neural Computation 10:12991319.
J. Hamm, D. D. Lee, S. Mika, and B. Schoelkopf (2004).
A kernel view of the dimensionality reduction of manifolds.
In Proceedings of the Twenty First International Confernence on Machine Learning (ICML04), pages 369376. Banff, Canada.
K. Q. Weinberger, F. Sha, and L. K. Saul (2004).
Learning a kernel matrix for nonlinear dimensionality reduction.
In Proceedings of the Twenty First International Confernence on Machine Learning
(ICML04), pages 839846. Banff, Canada.

