This class is designed for BTech Computer Science / AI / Data Science students who want to clearly understand Artificial Intelligence problem solving methods from basics to exam-oriented level. It is especially useful for students preparing for semester exams, GATE fundamentals, and concept building in AI.
In this course, students will learn how intelligent agents solve problems using different AI techniques. The syllabus includes problem formulation, state space representation, uninformed search methods (BFS, DFS, UCS, DLS, IDS), informed search methods (Greedy Best First Search, A* algorithm), heuristic functions, and comparison of search strategies. Each topic will be explained step by step with simple examples, diagrams, and pseudo-code so that students can easily understand complex concepts.
Special focus will be given to exam-oriented explanations, numerical problems, and theory questions commonly asked in BTech examinations. Students will also learn how to analyze time and space complexity of different AI search algorithms.
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