An Introduction to Artificial Intelligence

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.

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