CS 532
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|
Date |
Topic |
Reading |
Notes |
|
Jan. 7 |
Overview, Review of Propositional Logic |
Chapter 7 (review) |
|
|
Jan. 9 |
First-Order Logic: Syntax and Semantics |
Chapter 8 |
|
|
Jan. 11 |
FO Semantics cont., First-Order
Deduction |
Chapter 9 |
TXT |
|
Jan. 14 |
continued |
||
|
Jan. 16 |
continued |
||
|
Jan. 18 |
continued, First-Order Learning |
Sections 19.1,
19.5 |
TXT |
|
Jan. 21 |
MLK Day |
||
|
Jan. 23 |
continued |
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Jan. 25 |
continued |
||
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Jan. 28 |
continued, Weighted Logic | TXT |
|
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Jan. 30 |
continued |
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|
Feb. 1 |
continued |
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Feb. 4 |
Exam 1 |
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Feb. 6 |
continued |
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|
Feb. 8 |
cont., BN Review, Sum-Product |
14.1-14.4 | Class Notes |
|
Feb. 11 |
no class (will makeup) |
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Feb. 13 |
no class (will makeup) |
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Feb. 15 |
Sum-Product |
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|
Feb. 18 |
Max-Product and Belief Propogation |
"Pearl's Algorithm for Multiplexer Nodes" , Kevin Murphy, Technical Report, 1999 (read Section 1) |
Class Notes |
|
Feb. 20 |
Markov Chain Monte Carlo (MCMC): Markov Chains | pp. 516-518 | Instructor Notes 1 |
|
Feb. 22 |
MCMC: Gibb's Sampling | Instructor Notes 2 | |
|
Feb. 25 |
MCMC: Metropolis-Hastings, Sampling Strategies | Instructor Notes 3 | |
|
Feb. 27 |
cont. |
||
| Feb.
29 |
Relational Bayesian Networks (RBN) |
14.6 |
Instructor Notes |
|
March 3 |
Exam 2 |
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|
March 5 |
cont. RBN |
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|
March 7 |
Hidden Markov Models (HMMs) |
15.1, 15.2 | Instructor Notes |
|
March 10 |
HMM Inference | 15.2, 15.5 |
|
| March 12 |
Dynamic Bayes Nets and Particle Filtering | 15.2, 15.5 |
Instructor Notes |
| March 14 |
Review and Q/A | ||
| March 20 |
Final Exam, 2:00-3:50 |
.