An Introduction to Artificial Intelligence
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What you'll learn
This course includes
- 32.3 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 98 lessons • 32.3 hours of video
An Introduction to Artificial Intelligence
98 lessons
• 32.3 hours
An Introduction to Artificial Intelligence
98 lessons
• 32.3 hours
- An Introduction to Artificial Intelligence | Prof. Mausam 05:07
- Introduction: What to Expect from AI 14:06
- Introduction: History of AI from 40s - 90s 28:26
- Introduction: History of AI in the 90s 14:34
- Introduction: History of AI in NASA & DARPA(2000s) 21:05
- Introduction: The Present State of AI 20:40
- Introduction: Definition of AI Dictionary Meaning 25:06
- Introduction: Definition of AI Thinking VS Acting and Humanly VS Rationally 25:26
- Introduction: Definition of AI Rational Agent View of AI 26:03
- Introduction: Examples Tasks, Phases of AI & Course Plan 12:39
- Uniform Search: Notion of a State 12:24
- Uniformed Search: Search Problem and Examples Part-2 28:46
- Uniformed Search: Basic Search Strategies Part-3 34:21
- Uniformed Search: Iterative Deepening DFS Part-4 24:06
- Uniformed Search: Bidirectional Search Part-5 21:07
- Informed Search: Best First Search Part-1 22:01
- Informed Search: Greedy Best First Search and A* Search Part-2 12:03
- Informed Search: Analysis of A* Algorithm Part-3 19:33
- Informed Search Proof of optimality of A* Part-4 24:43
- Informed Search: Iterative Deepening A* and Depth First Branch & Bound Part-5 25:26
- Informed Search: Admissible Heuristics and Domain Relaxation Part-6 30:42
- Informed Search: Pattern Database Heuristics Part-7 21:32
- Local Search: Satisfaction Vs Optimization Part-1 19:22
- Local Search: The Example of N-Queens Part-2 13:27
- Local Search: Hill Climbing Part-3 18:19
- Local Search: Drawbacks of Hill Climbing Part-4 19:27
- Local Search: of Hill Climbing With random Walk & Random Restart Part-5 20:10
- Local Search: Hill Climbing With Simulated Anealing Part-6 20:53
- Local Search: Local Beam Search and Genetic Algorithms Part-7 32:44
- Adversarial Search : Minimax Algorithm for two player games 19:53
- Adversarial Search : An Example of Minimax Search 17:16
- Adversarial Search : Alpha Beta Pruning 16:50
- Adversarial Search : Analysis of Alpha Beta Pruning 20:14
- Adversarial Search : Analysis of Alpha Beta Pruning (contd...) 24:52
- Adversarial Search : Horizon Effect, Game Databases & Other Ideas 29:41
- Adversarial Search: Summary and Other Games 13:10
- Constraint Satisfaction Problems: Representation of the atomic state 15:19
- Constraint Satisfaction Problems: Map coloring and other examples of CSP 15:41
- Constraint Satisfaction Problems: Backtracking Search 11:13
- Constraint Satisfaction Problems: Variable and Value Ordering in Backtracking Search 19:46
- Constraint Satisfaction Problems: Inference for detecting failures early 25:06
- Constraint Satisfaction Problems: Exploiting problem structure 26:11
- Logic in AI : Different Knowledge Representation systems - Part 1 26:15
- Logic in AI : Syntax - Part - 2 16:34
- Logic in AI : Semantics - Part - 3 12:32
- Logic in AI : Forward Chaining - Part 4 10:09
- Logic in AI : Resolution - Part - 5 19:07
- Logic in AI : Reduction to Satisfiability Problems - Part - 6 30:01
- Logic in AI : SAT Solvers : DPLL Algorithm - Part - 7 25:22
- Logic in AI : Sat Solvers: WalkSAT Algorithm - Part - 8 09:34
- Uncertainty in AI: Motivation 10:00
- Uncertainty in AI: Basics of Probability 25:10
- Uncertainty in AI: Conditional Independence & Bayes Rule 27:16
- Bayesian Networks: Syntax 21:17
- Bayesian Networks: Factoriziation 15:28
- Bayesian Networks: Conditional Independences and d-Separation 34:37
- Bayesian Networks: Inference using Variable Elimination 24:27
- Bayesian Networks: Reducing 3-SAT to Bayes Net 06:05
- Bayesian Networks: Rejection Sampling 20:37
- Bayesian Networks: Likelihood Weighting 15:26
- Bayesian Networks: MCMC with Gibbs Sampling 13:52
- Bayesian Networks: Maximum Likelihood Learning" 18:16
- Bayesian Networks: Maximum a-Posteriori Learning 08:21
- Bayesian Networks: Bayesian Learning 09:37
- Bayesian Networks: Structure Learning and Expectation Maximization 15:09
- Introduction, Part 10: Agents and Environments 40:48
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