AI class readings

These are the suggested readings for the AI class taken from Artificial Intelligence: A Modern Approach.

Readings

• Week 1
  • 1.1 What Is AI? .............................................. 1
  • 1.4 The State of the Art ..................................... 28
  • 1.5 Summary .................................................. 29
  • 2.1 Agents and Environments .................................. 34
  • 2.2 Good Behavior: The Concept of Rationality ................ 36
  • 2.3 The Nature of Environments ............................... 40
  • 3.1 Problem-Solving Agents ................................... 64
  • 3.3 Searching for Solutions .................................. 75
  • 3.4 Uninformed Search Strategies ............................. 81
  • 3.5 Informed (Heuristic) Search Strategies ................... 92
• Week 2
  • 13.1 Acting under Uncertainty ................................ 480
  • 13.2 Basic Probability Notation .............................. 483
  • 13.3 Inference Using Full Joint Distributions ................ 490
  • 13.4 Independence ............................................ 494
  • 13.5 Bayes' Rule and Its Use ................................. 495
  • 14.1 Representing Knowledge in an Uncertain Domain ........... 510
  • 14.2 The Semantics of Bayesian Networks ...................... 513
  • 14.3 Efficient Representation of Conditional Distributions ... 518
  • 14.4 Exact Inference in Bayesian Networks .................... 522
  • 14.5 Approximate Inference in Bayesian Networks .............. 530
• Week 3
  • 18.1 Forms of Learning ....................................... 693
  • 18.2 Supervised Learning ..................................... 695
  • 18.3 Learning Decision Trees ................................. 698
  • 18.4 Evaluating and Choosing the Best Hypothesis ............. 708
  • 18.5 The Theory of Learning .................................. 713
  • 18.6 Regression and Classification with Linear Models ........ 717
  • 18.7 Artificial Neural Networks .............................. 727
• Week 4
  • 4.3 Searching with Nondeterministic Actions .................. 133
  • 4.4 Searching with Partial Observations ...................... 138
  • 10.1 Definition of Classical Planning ........................ 366
  • 10.2 Algorithms for Planning as State-Space Search ........... 373
  • 10.3 Planning Graphs ......................................... 379
  • 17.1 Sequential Decision Problems ............................ 645
  • 17.2 Value Iteration ......................................... 652
  • 17.3 Policy Iteration ........................................ 656
  • 17.4 Partially Observable MDPs ............................... 658