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The instructors
The instructors
Sebastian Thrun
Sebastian Thrun
Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning.
Peter Norvig
Peter Norvig
Peter Norvig is Director of Research at Google Inc. He is also a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Norvig is co-author of the popular textbook Artificial Intelligence: A Modern Approach. Prior to joining Google he was the head of the Computation Sciences Division at NASA Ames Research Center.
Readings
Readings
These suggested readings are taken from Artificial Intelligence: A Modern Approach, but you are welcome to review these topics from any source.
Week 10
Week 10
22.1. Language Models ... 860
22.2. Text Classification ... 865
22.3. Information Retrieval ... 867
Week 9
Week 9
25.4. Planning to Move ... 986
25.5. Planning Uncertain Movements ... 993
25.6. Moving ... 997
Week 8
Week 8
24.1. Image Formation ... 929
24.2. Early Image-Processing Operations ... 935
24.3. Object Recognition by Appearance ... 942
24.4. Reconstructing the 3D World ... 947
24.5. Object Recognition from Structural Information ... 957
24.6. Using Vision ... 961
Week 7
Week 7
5.1. Games ... 161
5.2. Optimal Decisions in Games ... 163
5.3. Alpha--Beta Pruning ... 167
5.4. Imperfect Real-Time Decisions ... 171
5.5. Stochastic Games ... 177
11.1. Time, Schedules, and Resources ... 401
11.2. Hierarchical Planning ... 406
11.3. Planning and Acting in Nondeterministic Domains ... 415
17.5. Decisions with Multiple Agents: Game Theory ... 666
17.6. Mechanism Design ... 679
Week 6
Week 6
15.1. Time and Uncertainty ... 566
15.2. Inference in Temporal Models ... 570
15.3. Hidden Markov Models ... 578
Week 5
Week 5
17.1. Sequential Decision Problems ... 645
17.2. Value Iteration ... 652
17.3. Policy Iteration ... 656
17.4. Partially Observable MDPs ... 658
21.2. Passive Reinforcement Learning ... 832
21.3. Active Reinforcement Learning ... 839
21.4. Generalization in Reinforcement Learning ... 845
21.5. Policy Search ... 848
21.6. Applications of Reinforcement Learning ... 850
21.7. Summary ... 853
Week 4
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
Week 3
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
Week 2
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 1
Week 1
1.1 What Is AI?
1.4 The State of the Art
1.5 Summary
2.1 Agents and Environments
2.2 Good Behavior: The Concept of Rationality
2.3 The Nature of Environments
3.1 Problem-Solving Agent
3.3 Searching for Solutions
3.4 Uninformed Search Strategies
3.5 Informed (Heuristic) Search Strategies
Prerequisites
Prerequisites
Some of the topics in Introduction to Artificial Intelligence will build on probability theory and linear algebra. To brush up, here are some related videos from Khan Academy. Watching these videos is not required, and you can probably do well in the class even if you are not initially familiar with these topics but are willing to work hard.
Probability Prerequisites
Probability (Part 6) [Conditional Probability]
Probability (Part 7) [Bayes' Rule]
Probability (Part 8) [More Bayes' Rule]
Introduction to Random Variables
Linear Algebra Prerequisites
Matrix Multiplication (Part 1)
Matrix Multiplication (Part 2)
Matrices to Solve a System of Equations
Vector Dot Product and Vector Length
Defining the Angle Between Vectors
Linear Transformations as Matrix Vector Products
Linear Transformation Examples: Scaling and Reflections
Linear Transformation Examples: Rotations in R2
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