Artificial Intelligence - Methods and Applications (Fall 13)

Reading instructions

In the reading instructions 'R&N' refers to Russel and Norvig, Artificial Intelligence, while 'Murphy' refers to Murphy, AI Robotics.

  • Lecture 1-2, 8th November: Introduction, Search heuristics, and Adversarial search. R&N 3.6-3.7, 5.0-5.4.
  • Lecture 3, 12th November: Review of FOPL. R&N 8.4-8.5, 9.1-9.5.
  • Lecture 4, 15th November: Reasoning with categories. R&N 12.1 – 12.5
  • Lecture 5-7, 17th and 19th November: Path finding, Localization, and Map Making. Murphy 7, 9-11. Compendium on forward kinematics.
  • Lecture 8, 29th November: Reasoning with uncertainty. R&N 13.1 – 13.3.
  • Lecture 9, 3rd December: Probabilistic reasoning. R&N 13.4 – 14.3. Compendium on learning bayesian networks.
  • Lecture 10, 6th December: Probabilistic reasoning over time. R&N 15.1 – 15.3.Compendium on hidden markov models
  • Lecture 11: Learning. R&N 18.3.4-18.3.6, 21.1-21.3.
  • Lecture 12: Grammatical inference. Compendium on learning algorithms, sections 2, 3.2, 4.
  • Lecture 13: Naive Bayesian Classifiers. R&N 22.1-22.2
  • Lecture 14: Bayesian learning, classical planning. R&N 20.1