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Course Content
Overview of Artificial Intelligence
- Introduction
- Definition
- Intelligent agents
Representation and search State Space Search
- Information on State Space Search
- Graph theory on State Space Search
- Problem-Solving through State Space Search
- Solution for State Space Search
- FSM
- BFS on Graph
- DFS algo
- DFS with iterative deepening
- backtracking algo
- trace backtracking on graph part 1
- trace backtracking on graph part 2
- Summary State Space Search
Representation and search Heuristic Search
- Heuristic Search Overview
- Heuristic Calculation technique part 1
- Heuristic Calculation technique part 2
- Simple hill climbing
- best first search algo
- tracing best first search 1
- best first search continue
- admissibility 1
- mini-max
- two ply min max
- alpha beta pruning
Machine Learning
- machine learning_overview
- perceptron learning
- perceptron with linearly separable
- backpropagation with multilayer neuron
- W for hidden node and back propagation algo
- backpropagation algorithm explained
- back propagation calculation_part01
- back propagation calculation_part02
- updation of weight and cluster
- k-means cluster‚NNalgo and application of machine learning
Logics and reasoning
- logics_reasoning_overview_propositional calculus part 1
- logics_reasoning_overview_propositional calculus part 2
- proportional calculus
- predicate calculus
- First order predicate calculus
- modus ponus‚tollens
- unification and deduction process
- resolution refutation
- resolution refutation in detail
- resolution refutation example-2 convert into clause
- resolution refutation example-2 apply refutation
- unification substitution and skolemization
- prolog overview_some part of reasoning
- model based and CBR reasoning
Rule-based Programming
- production system
- trace of production system
- knight tour prob in chessboard
- Goal driven_data driven production system part _ 1
- Goal driven_data driven production system part _ 2
- goal driven Vs data-driven and inserting and removing facts
- defining rules and commands
- CLIPS installation and clipstutorial 1
- CLIPS tutorial 2
- CLIPS tutorial 3
- CLIPS tutorial 4
- CLIPS tutorial 5_part01
- CLIPS tutorial 5_part02
- tutorial 6
- CLIPS tutorial 7
- CLIPS tutorial 8
- variable in pattern tutorial 9
- tutorial 10
- more on wildcardmatching_part01
- more on wildcardmatching_part02
- more on variables
- deffacts and deftemplates_part01
- deffacts and deftemplates_part02
- template in detail part1
- not operator
- for all and exists_part01
- for all and exists_part02
- truth and control
- tutorial 12
Decision Making
- intelligent agent
- simple reflex agent
- simple reflex agent with internal state
- goal based agent
- utility based agent
- basics of utility theory
- maximum expected utility
- decision theory and decision network
- reinforcement learning
- MDPand DDN
Stochastic methods
- basics of set theory part _ 1
- basics of set theory part _ 2
- probability distribution
- baysian rule for conditional probability
- examples of Bayes theorem