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intro
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Introduction: What to Expect from AI
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Introduction: History of AI from 40s - 90s
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Introduction: History of AI in the 90s
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Introduction: History of AI in NASA & DARPA(2000s)
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Introduction: The Present State of AI
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Introduction: Definition of AI Dictionary Meaning
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Introduction: Definition of AI Thinking VS Acting and Humanly VS Rationally
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Introduction: Definition of AI Rational Agent View of AI
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Introduction: Examples Tasks, Phases of AI & Course Plan
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Uniform Search: Notion of a State
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Uniformed Search: Search Problem and Examples Part-2
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Uniformed Search: Basic Search Strategies Part-3
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Uniformed Search: Iterative Deepening DFS Part-4
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Uniformed Search: Bidirectional Search Part-5
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Informed Search: Best First Search Part-1
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Informed Search: Greedy Best First Search and A* Search Part-2
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Informed Search: Analysis of A* Algorithm Part-3
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Informed Search Proof of optimality of A* Part-4
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Informed Search: Iterative Deepening A* and Depth First Branch & Bound Part-5
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Informed Search: Admissible Heuristics and Domain Relaxation Part-6
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Informed Search: Pattern Database Heuristics Part-7
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Local Search: Satisfaction Vs Optimization Part-1
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Local Search: The Example of N-Queens Part-2
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Local Search: Hill Climbing Part-3
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Local Search: Drawbacks of Hill Climbing Part-4
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Local Search: of Hill Climbing With random Walk & Random Restart Part-5
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Local Search: Hill Climbing With Simulated Anealing Part-6
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Local Search: Local Beam Search and Genetic Algorithms Part-7
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Adversarial Search : Minimax Algorithm for two player games
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Adversarial Search : An Example of Minimax Search
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Adversarial Search : Alpha Beta Pruning
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Adversarial Search : Analysis of Alpha Beta Pruning
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Adversarial Search : Analysis of Alpha Beta Pruning (contd...)
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Adversarial Search : Horizon Effect, Game Databases & Other Ideas
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Adversarial Search: Summary and Other Games
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Constraint Satisfaction Problems: Representation of the atomic state
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Constraint Satisfaction Problems: Map coloring and other examples of CSP
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Constraint Satisfaction Problems: Backtracking Search
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Constraint Satisfaction Problems: Variable and Value Ordering in Backtracking Search
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Constraint Satisfaction Problems: Inference for detecting failures early
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Constraint Satisfaction Problems: Exploiting problem structure
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Logic in AI : Different Knowledge Representation systems - Part 1
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Logic in AI : Syntax - Part - 2
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Logic in AI : Semantics - Part - 3
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Logic in AI : Forward Chaining - Part 4
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Logic in AI : Resolution - Part - 5
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Logic in AI : Reduction to Satisfiability Problems - Part - 6
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Logic in AI : SAT Solvers : DPLL Algorithm - Part - 7
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Logic in AI : Sat Solvers: WalkSAT Algorithm - Part - 8
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Uncertainty in AI: Motivation
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Uncertainty in AI: Basics of Probability
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Uncertainty in AI: Conditional Independence & Bayes Rule
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Bayesian Networks: Syntax
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Bayesian Networks: Factoriziation
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Bayesian Networks: Conditional Independences and d-Separation
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Bayesian Networks: Inference using Variable Elimination
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Bayesian Networks: Reducing 3-SAT to Bayes Net
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Bayesian Networks: Rejection Sampling
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Bayesian Networks: Likelihood Weighting
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Bayesian Networks: MCMC with Gibbs Sampling
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Bayesian Networks: Maximum Likelihood Learning"
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Bayesian Networks: Maximum a-Posteriori LearningÂ
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Bayesian Networks: Bayesian Learning
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Bayesian Networks: Structure Learning and Expectation Maximization
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Introduction, Part 10: Agents and Environments
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Description:
The course introduces a variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem. It also teaches many first algorithms to solve each formulation. The course prepares a student to take a variety of focused, advanced courses in various subfields of AI.

An Introduction to Artificial Intelligence

NPTEL
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#Computer Science #Artificial Intelligence #Humanities #Philosophy #Logic #Machine Learning #Probabilistic Graphical Models #Bayesian Networks #Algorithms #Search Algorithms #Constraint Satisfaction Problems
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intro