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- Intro: overview, agents, problem spaces
- Search: algorithms, heuristic generation, CSPs
- Knowledge representation: propositional and first-order logic, theorem
proving, subsumption, expressiveness-tractability tradeoff.
- Planning: time, actiona languages, regression search, SAT compile,
graphplan, planning as a CSP
- Learning: dececision trees, information gain, overfitting, bias,
ensembles, semi supervised learning
- Uncertainty: Bayes nets, Markov networks, statistical-relational
learning, learning, HMMs, DBNs, naive-bayes, EM
- Natural language: information extraction, sliding window, CRFs,
discriminative PCFGs
- Planning under uncertainty: MDPS, RTDP, reinforcement learning
- Special topics
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Department of Computer Science & Engineering
University of Washington
Box 352350
Seattle, WA 98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX
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