May 19, 2024
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Artificial Intelligence Tutorials for PU BS Students

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Artificial Intelligence (A Modern Approach 4th Edition) by Stuart Russell and Peter Norvig
Artificial Intelligence (A Modern Approach 4th Edition) by Stuart Russell and Peter Norvig
Week Wise Course Breakdown
WEEK 01LECTURE 0101 Introduction 1
1.1 What Is AI? 1
1.2 The Foundations of Artificial Intelligence  5
1.3 The History of Artificial Intelligence  17
 
LECTURE 021.4 The State of the Art 27
1.5 Risks and Benefits of AI  31
Test 01
02 Intelligent Agents 36
2.1 Agents and Environments  36
LECTURE 032.2 Good Behavior: The Concept of Rationality 39
2.3 The Nature of Environments 42
2.4 The Structure of Agents  47
Test 02
03 Solving Problems by Searching 63
WEEK 02LECTURE 043.1 Problem-Solving Agents 63
3.2 Example Problems 66
3.3 Search Algorithms 71
3.4 Uninformed Search Strategies  76
 
LECTURE 053.5 Informed (Heuristic) Search Strategies 84
3.6 Heuristic Functions 97
Test 03
04 Search in Complex Environments 110
4.1 Local Search and Optimization Problems  110
LECTURE 064.2 Local Search in Continuous Spaces 119
4.3 Search with Nondeterministic Actions  122
4.4 Search in Partially Observable Environments  126
4.5 Online Search Agents and Unknown Environments  134
Test 04
WEEK 03LECTURE 0705 Adversarial Search and Games 146
5.1 Game Theory 146
5.2 Optimal Decisions in Games  148
5.3 Heuristic Alpha–Beta Tree Search  156
 
LECTURE 085.4 Monte Carlo Tree Search 161
5.5 Stochastic Games 164
5.6 Partially Observable Games  168
5.7 Limitations of Game Search Algorithms  173
Test 05
LECTURE 0906 Constraint Satisfaction Problems 180
6.1 Defining Constraint Satisfaction Problems  180
6.2 Constraint Propagation: Inference in CSPs  185
6.3 Backtracking Search for CSPs  191
 
WEEK 04LECTURE 106.4 Local Search for CSPs 197
6.5 The Structure of Problems  199
Test 06
07 Logical Agents 208
7.1 Knowledge-Based Agents 209
LECTURE 117.2 The Wumpus World 210
7.3 Logic 214
7.4 Propositional Logic: A Very Simple Logic  217
7.5 Propositional Theorem Proving  222
 
LECTURE 127.6 Effective Propositional Model Checking 232
7.7 Agents Based on Propositional Logic  237
Test 07
08 First-Order Logic 251
8.1 Representation Revisited  251
WEEK 05LECTURE 138.2 Syntax and Semantics of First-Order Logic 256
8.3 Using First-Order Logic  265
8.4 Knowledge Engineering in First-Order Logic  271
Test 08
09 Inference in First-Order Logic 280
LECTURE 149.1 Propositional vs. First-Order Inference 280
9.2 Unification and First-Order Inference  282
9.3 Forward Chaining 286
9.4 Backward Chaining 293
 
LECTURE 159.5 Resolution 298
Test 09
10 Knowledge Representation 314
10.1 Ontological Engineering  314
10.2 Categories and Objects  317
WEEK 06LECTURE 1610.3 Events  322
10.4 Mental Objects and Modal Logic  326
10.5 Reasoning Systems for Categories  329
10.6 Reasoning with Default Information  333
 
LECTURE 17Test 10
11 Automated Planning 344
11.1 Definition of Classical Planning  344
11.2 Algorithms for Classical Planning 348
 
LECTURE 1811.3 Heuristics for Planning 353
11.4 Hierarchical Planning 356
11.5 Planning and Acting in Nondeterministic Domains  365
11.6 Time, Schedules, and Resources  374
11.7 Analysis of Planning Approaches  378
WEEK 07LECTURE 19Test 11
12 Quantifying Uncertainty 385
12.1 Acting under Uncertainty  385
12.2 Basic Probability Notation  388
 
LECTURE 2012.3 Inference Using Full Joint Distributions 395
12.4 Independence 397
12.5 Bayes’ Rule and Its Use  399
12.6 Naive Bayes Models 402
12.7 The Wumpus World Revisited  404
LECTURE 21Test 12
13 Probabilistic Reasoning 412
13.1 Representing Knowledge in an Uncertain Domain  412
13.2 The Semantics of Bayesian Networks  414
13.3 Exact Inference in Bayesian Networks  427
WEEK 08LECTURE 2213.4 Approximate Inference for Bayesian Networks 435
13.5 Causal Networks 449
Test 13
14 Probabilistic Reasoning over Time 461
 
LECTURE 2314.1 Time and Uncertainty 461
14.2 Inference in Temporal Models  465
14.3 Hidden Markov Models 473
14.4 Kalman Filters 479
 
LECTURE 2414.5 Dynamic Bayesian Networks 485
Test 14
15 Probabilistic Programming 500
15.1 Relational Probability Models  501
15.2 Open-Universe Probability Models  507
WEEK 09LECTURE 2515.3 Keeping Track of a Complex World 514
15.4 Programs as Probability Models  519
Test 15
16 Making Simple Decisions 528
16.1 Combining Beliefs and Desires under Uncertainty  528
LECTURE 2616.2 The Basis of Utility Theory 529
16.3 Utility Functions 532
16.4 Multiattribute Utility Functions  540
16.5 Decision Networks 544
16.6 The Value of Information  547
LECTURE 2716.7 Unknown Preferences 553
Test 16
17 Making Complex Decisions 562
17.1 Sequential Decision Problems  562
 
WEEK 10LECTURE 2817.2 Algorithms for MDPs 572
17.3 Bandit Problems 581
17.4 Partially Observable MDPs  588
17.5 Algorithms for Solving POMDPs  590
Test 17
LECTURE 2918 Multiagent Decision Making 599
18.1 Properties of Multiagent Environments  599
18.2 Non-Cooperative Game Theory  605
18.3 Cooperative Game Theory  626
 
LECTURE 3018.4 Making Collective Decisions 632
Test 18
19 Learning from Examples 651
19.1 Forms of Learning 651
19.2 Supervised Learning 653
WEEK 11LECTURE 3119.3 Learning Decision Trees 657
19.4 Model Selection and Optimization  665
19.5 The Theory of Learning  672
19.6 Linear Regression and Classification  676
 
LECTURE 3219.7 Nonparametric Models 686
19.8 Ensemble Learning 696
19.9 Developing Machine Learning Systems  704
Test 19
20 Learning Probabilistic Models 721
LECTURE 3320.1 Statistical Learning 721
20.2 Learning with Complete Data  724
20.3 Learning with Hidden Variables: The EM Algorithm  737
Test 20
21 Deep Learning 750
WEEK 12LECTURE 3421.1 Simple Feedforward Networks 751
21.2 Computation Graphs for Deep Learning  756
21.3 Convolutional Networks  760
21.4 Learning Algorithms 765
 
LECTURE 3521.5 Generalization 768
21.6 Recurrent Neural Networks  772
21.7 Unsupervised Learning and Transfer Learning  775
21.8 Applications 782
Test 21
LECTURE 3622 Reinforcement Learning 789
22.1 Learning from Rewards 789
22.2 Passive Reinforcement Learning  791
22.3 Active Reinforcement Learning  797
 
WEEK 13Lecture 3722.4 Generalization in Reinforcement Learning 803
22.5 Policy Search 810
22.6 Apprenticeship and Inverse Reinforcement Learning  812
22.7 Applications of Reinforcement Learning  815
Test 22
LECTURE 3823 Natural Language Processing 823
23.1 Language Models 823
23.2 Grammar 833
23.3 Parsing 835
 
Lecture 3923.4 Augmented Grammars 841
23.5 Complications of Real Natural Language  845
23.6 Natural Language Tasks  849
Test 23
 
WEEK 14Lecture 4024 Deep Learning for Natural Language Processing 856
24.1 Word Embeddings 856
24.2 Recurrent Neural Networks for NLP  860
24.3 Sequence-to-Sequence Models  864
 
Lecture 4124.4 The Transformer Architecture 868
24.5 Pretraining and Transfer Learning  871
24.6 State of the art 875
Test 24
25 Computer Vision 881
Lecture 4225.1 Introduction 881
25.2 Image Formation 882
25.3 Simple Image Features 888
25.4 Classifying Images 895
 
WEEK 15Lecture 4325.5 Detecting Objects 899
25.6 The 3D World 901
25.7 Using Computer Vision 906
Test 25
26 Robotics 925
Lecture 4426.1 Robots  925
26.2 Robot Hardware 926
26.3 What kind of problem is robotics solving?  930
 
 
Lecture 4526.4 Robotic Perception 931
26.5 Planning and Control 938
26.6 Planning Uncertain Movements 956
26.7 Reinforcement Learning in Robotics 958
 
WEEK 16Lecture 4626.8 Humans and Robots 961
26.9 Alternative Robotic Frameworks  968
26.10 Application Domains 971
Test 26
27 Philosophy, Ethics, and Safety of AI 981
Lecture 4727.1 The Limits of AI 981
27.2 Can Machines Really Think?  984
27.3 The Ethics of AI 986
Test 27
 
Lecture 4828 The Future of AI 1012
28.1 AI Components 1012
28.2 AI Architectures 1018
Test 28
 
Artificial Intelligence (A Modern Approach 4th Edition) by Stuart Russell and Peter Norvig

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