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| All downloadable documents are Adobe Acrobat PDF documents. You can obtain Acrobat for free by following the link from the Adobe Icon.
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| Date
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Topic
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Assignments
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Aug. 27
| Introduction
| Background Readings:
Linear Algebra Refresher,
Statistics Refresher,
Issues in Neural Networks
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Sept. 3
| An Introduction to Graphical Models and Statistical Estimation
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Ch.2,
Ch.3,
Ch.5,
Questions Ch.2 & Ch.3,
Questions Ch.5
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Sept. 10
| Refreshing Classification and Regression
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Ch.6,
Ch.7,
Questions Ch.6,
Questions Ch.7
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Sept. 17
| Maximum Likelihood Estimation & Mixture Models
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Ch.10,
Ch.13
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Sept. 24
| The Theory of the EM Algorithm
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Ch.10,
Ch.11,
Questions Ch.10,
Questions Ch.11,
EM-background slides
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Oct.1
| The EM Algorithm applied to mixture models and factor analsis
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Paper on Factor Analysis,
Ch.14,
Derivation of Factor Analysis
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Oct. 8
| Bayesian Model Estimation: Basics & Gaussian Approximation
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B_Ch10
Paper on Bayesian PCA,
Handout on Bayesian Factor Analysis
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Oct. 15
| Bayesian Model Estimation: Variational Approximations
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Variational Tutorial,
Variational Bayes,
Calculus of Variations,
Variational Bayesian Factor Analysis
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Oct 22
| Bayesian Model Estimation: Variational Methods (continued), and
of Markov Chain Monte Carlo Methods
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Variational PCA Paper,
MacKay Ch32 & Ch33
B_CH10.9
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Oct. 29
| Hidden Markov Models
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Ch.12,
HMM-paper
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Nov. 5
| Hidden Markov Models (cont'd)
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Ch.15,
HMM-paper
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Nov. 12
| Kalman Filters, Particle Filters
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Ch.15,
Intro-Paper
Applied-Paper
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Nov. 19
| Gaussian Processes
| Paper_MacKay,
Paper_Williams,
Questions
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Nov. 26
| Support Vector Machines
| Tutorial_Burges,
Tutorial SVM Regression,
Class notes
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Dec.3
| Student Presentations
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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
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