Site Search  






Teaching » Syllabus: Advanced Topics in Neural Computation and Statistical Learning

All downloadable documents are Adobe Acrobat PDF documents. You can obtain Acrobat for free by following the link from the Adobe Icon.



Aug. 27

Background Readings:

Linear Algebra Refresher,
Statistics Refresher,
Issues in Neural Networks

Sept. 3

An Introduction to Graphical Models and Statistical Estimation

Ch.2, Ch.3, Ch.5, Questions Ch.2 & Ch.3, Questions Ch.5

Sept. 10

Refreshing Classification and Regression

Ch.6, Ch.7, Questions Ch.6, Questions Ch.7

Sept. 17

Maximum Likelihood Estimation & Mixture Models

Ch.10, Ch.13

Sept. 24

The Theory of the EM Algorithm

Ch.10, Ch.11, Questions Ch.10, Questions Ch.11, EM-background slides


The EM Algorithm applied to mixture models and factor analsis

Paper on Factor Analysis, Ch.14, Derivation of Factor Analysis

Oct. 8

Bayesian Model Estimation: Basics & Gaussian Approximation

B_Ch10 Paper on Bayesian PCA, Handout on Bayesian Factor Analysis

Oct. 15

Bayesian Model Estimation: Variational Approximations

Variational Tutorial, Variational Bayes, Calculus of Variations, Variational Bayesian Factor Analysis

Oct 22

Bayesian Model Estimation: Variational Methods (continued), and

of Markov Chain Monte Carlo Methods

Variational PCA Paper, MacKay Ch32 & Ch33 B_CH10.9

Oct. 29

Hidden Markov Models

Ch.12, HMM-paper

Nov. 5

Hidden Markov Models (cont'd)

Ch.15, HMM-paper

Nov. 12

Kalman Filters, Particle Filters

Ch.15, Intro-Paper Applied-Paper

Nov. 19

Gaussian Processes Paper_MacKay,

Paper_Williams, Questions

Nov. 26

Support Vector Machines Tutorial_Burges,

Tutorial SVM Regression, Class notes


Student Presentations

All book chapter in PS and PDF format:

  • Chapter 2: Conditional Independence and Factorization, PDF, PS
  • Chapter 3: The Elimination Algorithm, PDF, PS
  • Chapter 4: Probability Propagation and Factor Graphs, PDF, PS
  • Chapter 5: Statistical Concepts, PDF, PS
  • Chapter 6: Linear Regression and the LMS Algorithm, PDF, PS
  • Chapter 7: Linear Classification, PDF, PS
  • Chapter 8: The Exponential Family and Generalized Linear Models, PDF, PS
  • Chapter 9: Completely Observed Graphical Models, PDF, PS
  • Chapter 10: Mixtures and conditional Mixtures, PDF, PS
  • Chapter 11: The EM Algorithm, PDF, PS
  • Chapter 12: Hidden Markov Models, PDF, PS
  • Chapter 13: The Multivariate Gaussians, PDF, PS
  • Chapter 14: Factor Analysis, PDF, PS
  • Chapter 15: Kalman Filtering and Smoothing, PDF, PS
  • Chapter 16: not available yet
  • Chapter 17: The Junction Tree Algorithm, PDF, PS
  • Chapter 18: The HMM and State Space Model Revisited, PDF, PS
  • Chapter 19: not available yet
  • Chapter 20: Features, Maximum Entropy, and Duality, PDF, PS
  • Chapter 21: Iterative Scaling Algorithms, PDF, PS
Designed by: Nerses Ohanyan & Jan Peters
Page last modified on January 25, 2006, at 06:59 PM