Thursdays 11:00-12:00
Building 81 Lobby Conference Room
| date | topic | readings and homework | lecture slides and demos |
| February 15 | Search and Planning | R&N Ch 3; Ch 4 up to end of 4.4. homework: Planning with Shakey video clip: the real Shakey the robot from the SRI AI Center in 1969 |
search and planning lecture slides: |
| February 22 | NO CLASS |
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| March 1 | Local Search and |
Ch 4.3-4.4 homework: Scheduling and Rectangle Packing by CSP |
local search and csp slides: |
| March 8 | NO CLASS |
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| March 15 | Propositional Logic | Ch 7.3-7.6 homework: Solving Sudoku by satisfiability testing |
propositional logics slides: planning as satisfiability using blackbox |
| March 22 | First-order logic and Logic programming | Ch 8; Ch 9.1-9.4; Ch 10.6 homework: Solving the Zebra Puzzle with Prolog Mini-zebra puzzle solution |
first-order logic slides: |
| March 29 | Graphical probabilistic models | R&N Ch 14.1; Bishop 8.2-8.3 | Christopher Bishop: Graphical Models and Variational Methods. A set of 8 tutorials given at the Machine Learning Summer School on Berder Island, 2004. Full-screen pdf 6-up handout pdf |
| April 5 | NO CLASS | ||
| April 12 | NO CLASS | ||
| April 19 | Inference in graphical models | Bishop 8.4; R&N 14.5 homework: Building a Bayesian network for reasoning about genetic inheritance |
Bishop slides, continued.
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| April 26 | continued |
Leonid Sigal: Graphical Models for Pose Estimation |
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| May 3 | continued | Pedro Domingos: |
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| May 10 | Decision trees and boosting | R&N Ch 18 | R&N: Learning from Observations |
| May 17 | NO CLASS |
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| May 24 | NO CLASS |
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| May 31 | NO CLASS |
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| June 7 | NO CLASS |
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| June 14 | NO CLASS |
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| June 21 | NO CLASS | ||
| June 28 | NO CLASS |
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| July 5 | NO CLASS | ||
| July 12 | Statistical learning and neural networks | R&N Ch 20.1-20.4 R&N Ch 20.5, 20.7 |
R&N: Statistical Learning R&N: Neural Networks Neural net character recognition applet |
| July 19 | Support Vector Machines | R&N Ch 20.6 | Support Vector Machine slides by Joe Michael Kniss A Simple Introduction to Support Vector Machines slides by Martin Law |