4-Course Content :-

Topic

A Fundamental of AI: Introduction, The AI Problems, History of AI.

A Fundamental of AI: Inspirations, Representations/Languages Used, General Tasks to Accomplish, Generic Techniques Developed, AI’s Applications and Products. 

Problems, Problem Spaces and Search: Problem and Problem spaces concepts, Define the problem as a state space search.

Problems, Problem Spaces and Search: Define the problem as a state space search, Characteristics of problem spaces.

Search Techniques: Introduction, Uninformed Search Strategies, Breadth First Search, Depth First Search.

Search Techniques: Uninformed Search Strategies, Iterative Deepening Search, Bidirectional Search, Uniform Path Cost Search. Heuristic Search Strategies, Best First Search, A* Search.

Search Techniques: Heuristic Search Strategies, IDA* Search, Hill Climbing, Simulated Annealing, Random Search, Problem Reduction Search. 

Knowledge Representation: Introduction, The Role of Knowledge, Semantic Networks.

Knowledge Representation: Frames, Propositional Logic, Deductive Reasoning with Propositional Logic, Limitations of Propositional Logic.

Knowledge Representation: First-Order Logic (Predicate Logic), Atomic Sentences, Compound Sentences, Variables, Quantifiers.

Machine Learning: What is Machine Learning?  Machine Learning Algorithms.

Machine Learning: Supervised Learning, Learning with Decision Trees, Creating a Decision Tree, Characteristics of Decision-Tree Learning.

Machine Learning: Unsupervised Learning, Markov Models, Word-Form Learning with Markov Chains, Word Generation with Markov Chains, Other Applications of Markov Chains, Nearest Neighbor Classification, 1NN Example, k-NN Example.

Machine Learning: Example of Famous machine learning algorithm, Neural Network, Genetic Algorithm.

Total