Assignment Schedule:
| Weekly Materials | M |
T |
W |
R |
F |
| Week #1 | April 2 |
3 |
4 |
5 |
6 |
Introduction and Markov Models |
Lecture 01 |
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| Week #2 | 9 |
10 |
11 |
12 |
13 |
Markov Models |
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| Week #3 | 16 |
17 |
18 |
19 |
20 |
Latent Semantic Indexing |
|
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| Week #4 | 23 |
24 |
25 |
26 |
27 |
LSI and Hidden Markov Models |
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| Week #5 | 30 |
May 1 |
2 |
3 |
4 |
Hidden Markov Models |
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| Week #6 | 7 |
8 |
9 |
10 |
11 |
HMMs and Dynamic Bayesian Networks |
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| Week #7 | 14 |
15 |
16 |
17 |
18 |
GMTK |
Lecture 13 no class |
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| Week #8 | 21 |
22 |
23 |
24 |
25 |
Particle Filters |
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| Week #9 | 28 |
29 |
30 |
31 |
June 1 |
More Particle Filters |
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|
Lecture 18 Particle Filter presentations: 1) Robust Monte Carlo Localization for Mobile Robots Artificial Intelligence (AI), 2000 (?) 2) Particle Filters for Location Estimation in
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GMTK on Place Lab |
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| Week #10 | 4 |
5 |
6 |
7 |
8 |
Collaborative Filtering |
Lecture 19 Collaborative Filtering Presentations 1) Chapter 2 of Marlin's Thesis:Collaborative Filtering: A Machine Learning Perspective (Formulations) (Sara) * Slides 2)Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems (Sameer)
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Lecture 20 Collborative Filtering Presentation 1) Latent Dirichlet Allocation (Nathan) 2)Chapter 6 of Marlin's Thesis (Dimensionality Reduction) (Chaitanya) |
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| Finals Week | 11 |
12 |
13 |
14 |
15 |
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