An Introduction to Artificial Intelligence
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(2)
45 learners
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. Mausam05:07
- Introduction: What to Expect from AI14:06
- Introduction: History of AI from 40s - 90s28:26
- Introduction: History of AI in the 90s14:34
- Introduction: History of AI in NASA & DARPA(2000s)21:05
- Introduction: The Present State of AI20:40
- Introduction: Definition of AI Dictionary Meaning25:06
- Introduction: Definition of AI Thinking VS Acting and Humanly VS Rationally25:26
- Introduction: Definition of AI Rational Agent View of AI26:03
- Introduction: Examples Tasks, Phases of AI & Course Plan12:39
- Uniform Search: Notion of a State12:24
- Uniformed Search: Search Problem and Examples Part-228:46
- Uniformed Search: Basic Search Strategies Part-334:21
- Uniformed Search: Iterative Deepening DFS Part-424:06
- Uniformed Search: Bidirectional Search Part-521:07
- Informed Search: Best First Search Part-122:01
- Informed Search: Greedy Best First Search and A* Search Part-212:03
- Informed Search: Analysis of A* Algorithm Part-319:33
- Informed Search Proof of optimality of A* Part-424:43
- Informed Search: Iterative Deepening A* and Depth First Branch & Bound Part-525:26
- Informed Search: Admissible Heuristics and Domain Relaxation Part-630:42
- Informed Search: Pattern Database Heuristics Part-721:32
- Local Search: Satisfaction Vs Optimization Part-119:22
- Local Search: The Example of N-Queens Part-213:27
- Local Search: Hill Climbing Part-318:19
- Local Search: Drawbacks of Hill Climbing Part-419:27
- Local Search: of Hill Climbing With random Walk & Random Restart Part-520:10
- Local Search: Hill Climbing With Simulated Anealing Part-620:53
- Local Search: Local Beam Search and Genetic Algorithms Part-732:44
- Adversarial Search : Minimax Algorithm for two player games19:53
- Adversarial Search : An Example of Minimax Search17:16
- Adversarial Search : Alpha Beta Pruning16:50
- Adversarial Search : Analysis of Alpha Beta Pruning20:14
- Adversarial Search : Analysis of Alpha Beta Pruning (contd...)24:52
- Adversarial Search : Horizon Effect, Game Databases & Other Ideas29:41
- Adversarial Search: Summary and Other Games13:10
- Constraint Satisfaction Problems: Representation of the atomic state15:19
- Constraint Satisfaction Problems: Map coloring and other examples of CSP15:41
- Constraint Satisfaction Problems: Backtracking Search11:13
- Constraint Satisfaction Problems: Variable and Value Ordering in Backtracking Search19:46
- Constraint Satisfaction Problems: Inference for detecting failures early25:06
- Constraint Satisfaction Problems: Exploiting problem structure26:11
- Logic in AI : Different Knowledge Representation systems - Part 126:15
- Logic in AI : Syntax - Part - 216:34
- Logic in AI : Semantics - Part - 312:32
- Logic in AI : Forward Chaining - Part 410:09
- Logic in AI : Resolution - Part - 519:07
- Logic in AI : Reduction to Satisfiability Problems - Part - 630:01
- Logic in AI : SAT Solvers : DPLL Algorithm - Part - 725:22
- Logic in AI : Sat Solvers: WalkSAT Algorithm - Part - 809:34
- Uncertainty in AI: Motivation10:00
- Uncertainty in AI: Basics of Probability25:10
- Uncertainty in AI: Conditional Independence & Bayes Rule27:16
- Bayesian Networks: Syntax21:17
- Bayesian Networks: Factoriziation15:28
- Bayesian Networks: Conditional Independences and d-Separation34:37
- Bayesian Networks: Inference using Variable Elimination24:27
- Bayesian Networks: Reducing 3-SAT to Bayes Net06:05
- Bayesian Networks: Rejection Sampling20:37
- Bayesian Networks: Likelihood Weighting15:26
- Bayesian Networks: MCMC with Gibbs Sampling13:52
- Bayesian Networks: Maximum Likelihood Learning"18:16
- Bayesian Networks: Maximum a-Posteriori LearningÂ08:21
- Bayesian Networks: Bayesian Learning09:37
- Bayesian Networks: Structure Learning and Expectation Maximization15:09
- Introduction, Part 10: Agents and Environments40:48
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