Introduces core ideas in Artificial Intelligence (AI). Heuristic versus algorithmic methods; problem solving; game playing and decision making; automatic theorem proving; pattern recognition; adaptive learning; projects to illustrate theoretical concepts. The learning outcomes of this course are to understand Intelligent Systems and Intelligent Techniques.
What is AI? Problem Solving Agent, Problem Elements, Solving Problems by Searching, Search Strategies, Comparison, Handling Repeated States, Informed Search Methods, Local Search Methods, Constraint Satisfaction Problem (CSP), Game Playing, Knowledge and Reasoning, Propositional Logic, First-Order-Logic, Uncertain Knowledge and Reasoning, Basics of Probability, Using Bayes' Rule for Reasoning, Constructing Bayesian Networks, Inference in Bayesian Networks, Making Simple Decisions, Statistical Learning, Probabilistic Reasoning over Time, Learning with Trees.
There are some projects which I have implemeted in this course. Please have a look at the project file description for more details.