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