Steam-powered Turing Machine University of Washington Department of Computer Science & Engineering
 CSE573 Course Overview
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 Topic Overview
 Slides & Reading
 Reading Reports
 Problem sets
  • 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

      CSE logo Department of Computer Science & Engineering
      University of Washington
      Box 352350
      Seattle, WA  98195-2350
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