Playlist Duration

Playlist · NPTEL IIT Delhi

An Introduction to Artificial Intelligence

by NPTEL IIT Delhi · 99 videos

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1d 8h 29m

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The shape of the course

99 videos in blocks of 10 · each column is a block's total, to scale

3h 13m
3h 45m
3h 36m
3h 4m
3h 21m
3h 20m
2h 58m
3h 5m
3h 36m
2h 31m

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Videos (99)

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Videos to
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1 An Introduction to Artificial Intelligence | Prof. Mausam 5:07 2020-01-02
2 Introduction: What to Expect from AI 14:06 2020-01-16
3 Introduction: History of AI from 40s - 90s 28:26 2020-01-24
4 Introduction: History of AI in the 90s 14:34 2020-01-16
5 Introduction: History of AI in NASA & DARPA(2000s) 21:05 2020-01-16
6 Introduction: The Present State of AI 20:40 2020-01-16
7 Introduction: Definition of AI Dictionary Meaning 25:06 2020-01-16
8 Introduction: Definition of AI Thinking VS Acting and Humanly VS Rationally 25:26 2020-01-16
9 Introduction: Definition of AI Rational Agent View of AI 26:03 2020-01-16
10 Introduction: Examples Tasks, Phases of AI & Course Plan 12:39 2020-01-16
11 Uniform Search: Notion of a State 12:24 2020-01-29
12 Uniformed Search: Search Problem and Examples Part-2 28:46 2020-01-29
13 Uniformed Search: Basic Search Strategies Part-3 34:21 2020-01-29
14 Uniformed Search: Iterative Deepening DFS Part-4 24:06 2020-01-29
15 Uniformed Search: Bidirectional Search Part-5 21:07 2020-01-29
16 Informed Search: Best First Search Part-1 22:01 2020-01-29
17 Informed Search: Greedy Best First Search and A* Search Part-2 12:03 2020-01-29
18 Informed Search: Analysis of A* Algorithm Part-3 19:33 2020-01-29
19 Informed Search Proof of optimality of A* Part-4 24:43 2020-01-29
20 Informed Search: Iterative Deepening A* and Depth First Branch & Bound Part-5 25:26 2020-01-29
21 Informed Search: Admissible Heuristics and Domain Relaxation Part-6 30:42 2020-02-07
22 Informed Search: Pattern Database Heuristics Part-7 21:32 2020-02-07
23 Local Search: Satisfaction Vs Optimization Part-1 19:22 2020-02-14
24 Local Search: The Example of N-Queens Part-2 13:27 2020-02-14
25 Local Search: Hill Climbing Part-3 18:19 2020-02-14
26 Local Search: Drawbacks of Hill Climbing Part-4 19:27 2020-02-14
27 Local Search: of Hill Climbing With random Walk & Random Restart Part-5 20:10 2020-02-14
28 Local Search: Hill Climbing With Simulated Anealing Part-6 20:53 2020-02-14
29 Local Search: Local Beam Search and Genetic Algorithms Part-7 32:44 2020-02-14
30 Adversarial Search : Minimax Algorithm for two player games 19:53 2020-02-21
31 Adversarial Search : An Example of Minimax Search 17:16 2020-02-21
32 Adversarial Search : Alpha Beta Pruning 16:50 2020-02-21
33 Adversarial Search : Analysis of Alpha Beta Pruning 20:14 2020-02-21
34 Adversarial Search : Analysis of Alpha Beta Pruning (contd...) 24:52 2020-03-05
35 Adversarial Search : Horizon Effect, Game Databases & Other Ideas 29:41 2020-02-21
36 Adversarial Search: Summary and Other Games 13:10 2020-02-26
37 Constraint Satisfaction Problems: Representation of the atomic state 15:19 2020-02-28
38 Constraint Satisfaction Problems: Map coloring and other examples of CSP 15:41 2020-02-28
39 Constraint Satisfaction Problems: Backtracking Search 11:13 2020-02-28
40 Constraint Satisfaction Problems: Variable and Value Ordering in Backtracking Search 19:46 2020-02-28
41 Constraint Satisfaction Problems: Inference for detecting failures early 25:06 2020-02-28
42 Constraint Satisfaction Problems: Exploiting problem structure 26:11 2020-02-28
43 Logic in AI : Different Knowledge Representation systems - Part 1 26:15 2020-03-06
44 Logic in AI : Syntax - Part - 2 16:34 2020-03-06
45 Logic in AI : Semantics - Part - 3 12:32 2020-03-06
46 Logic in AI : Forward Chaining - Part 4 10:09 2020-03-06
47 Logic in AI : Resolution - Part - 5 19:07 2020-03-06
48 Logic in AI : Reduction to Satisfiability Problems - Part - 6 30:01 2020-03-06
49 Logic in AI : SAT Solvers : DPLL Algorithm - Part - 7 25:22 2020-03-06
50 Logic in AI : Sat Solvers: WalkSAT Algorithm - Part - 8 9:34 2020-03-06
51 Uncertainty in AI: Motivation 10:00 2020-03-13
52 Uncertainty in AI: Basics of Probability 25:10 2020-03-13
53 Uncertainty in AI: Conditional Independence & Bayes Rule 27:16 2020-03-13
54 Bayesian Networks: Syntax 21:17 2020-03-13
55 Bayesian Networks: Factoriziation 15:28 2020-03-13
56 Bayesian Networks: Conditional Independences and d-Separation 34:37 2020-03-13
57 Bayesian Networks: Inference using Variable Elimination 24:27 2020-03-13
58 Bayesian Networks: Reducing 3-SAT to Bayes Net 6:05 2020-03-13
59 Bayesian Networks: Rejection Sampling 20:37 2020-03-19
60 Bayesian Networks: Likelihood Weighting 15:26 2020-03-19
61 Bayesian Networks: MCMC with Gibbs Sampling 13:52 2020-03-19
62 Bayesian Networks: Maximum Likelihood Learning" 18:16 2020-03-19
63 Bayesian Networks: Maximum a-Posteriori Learning 8:21 2020-03-19
64 Bayesian Networks: Bayesian Learning 9:37 2020-03-19
65 Bayesian Networks: Structure Learning and Expectation Maximization 15:09 2020-03-19
66 Introduction, Part 10: Agents and Environments 40:48 2020-03-19
67 mod10lec66 15:54 2020-03-29
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98 mod12lec96 5:17 2020-03-31
99 Artificial Intelligence: Foundations and Algorithms (Intro Video) 6:28 2026-06-01
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