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 ................................. 697
- 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