Practical Probabilistic
Modeling with Graphical Models
EE 639 Advanced Topics in Signal Processing
and Communication
Fall 2009
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Dates |
Lecture |
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8/26 |
Reading:
“Graphical Models” by M. I. Jordan (focus on
applications) Introduction to the course (updated: 8/28) |
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8/28 – 9/14 |
Reading:
Chapter 2 Preliminaries (update: 8/31) |
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9/16 |
Reading:
Chapter 3 |
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9/21-9/23 |
Reading:
Chapter 4 |
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9/28-10/5 |
Application: Error Control Coding and Loopy Belief
Propagation |
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10/7 – 10/12 |
Reading:
Chapter 5 |
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10/14 -10/21 |
Reading:
Chapter 6 |
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10/26-10/28 |
Reading:
Chapter 7, 8 Linear Classification (1) (2) (3) The exponential family and generalized linear models |
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11/2 – 11/4 |
Reading:
Chapter 9, 10 Completely Observed Graphical Models (1) Mixtures and conditional mixtures (1) |
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11/9 |
Reading:
Chapter 11 Expectation Maximization (1) |
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11/16 |
Reading:
Chapter 12 |
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Application: Speech Recognition and Synthesis |
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11/16 – 11/18 |
Reading:
Chapter 13, 15, 18 The Multivariate Gaussian Kalman Filtering The HMM and State Space Model Revisited |
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Application: Object Tracking |
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11/23 – 11/30 |
Reading:
Markov Properties Reading:
Chapter 17 Junction Tree |
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Skipped? |
Reading:
Chapter 19 Features, maximum entropy, and duality |
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Skipped? |
Reading:
Chapter 20 Iterative scaling algorithms |
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12/2 |
Reading:
Sampling Methods |
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Application: Object Tracking with Particle
Filters |
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12/4 |
Final project poster presentation |
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12/7 – 12/11 |
Reading:
Graphical models, exponential families and variational
inference |
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Application: Image Segmentation |
Last update: 8/24/2009