This lecture presents problem reduction search techniques, specifically focusing on AND/OR graphs. Students will understand how to decompose complex problems into simpler subproblems for effective resolution.
This lecture introduces the foundational concepts of Artificial Intelligence, its significance, and its impact on various fields. Students will explore the historical context and current advancements, setting the stage for deeper learning.
This lecture delves into problem-solving techniques utilizing search strategies. Students will learn about different search algorithms, their applications, and how they can be implemented to find solutions in various scenarios.
This module covers the concept of searching with costs, emphasizing how to evaluate and optimize paths in problem-solving scenarios. It will introduce cost functions and their importance in search algorithms.
This lecture introduces informed state space search methods. Students will learn how to utilize heuristics to improve search efficiency and effectiveness in problem-solving.
This module focuses on heuristic search methods, particularly the A* algorithm and its variants. Students will gain insights into how heuristics guide the search process in AI applications.
This lecture presents problem reduction search techniques, specifically focusing on AND/OR graphs. Students will understand how to decompose complex problems into simpler subproblems for effective resolution.
This module covers searching game trees, introducing concepts related to adversarial search and strategies for optimizing decision-making in competitive environments.
This lecture introduces knowledge-based systems, emphasizing logic and deduction. Students will explore the structures of knowledge representation and the role of logic in AI.
This module covers First Order Logic, presenting its syntax and semantics. Students will learn how to use it for knowledge representation and reasoning in intelligent systems.
This lecture dives into inference in First Order Logic, teaching students how to derive conclusions and make deductions based on logical statements.
This module explains resolution-refutation proofs, a fundamental technique in automated theorem proving. Students will learn how to construct proofs and understand their significance in AI.
This module continues the exploration of resolution-refutation proofs, providing deeper insights and practical examples to enhance understanding and application in AI.
This lecture introduces Logic Programming, focusing on Prolog as a primary language. Students will learn the basic concepts of Prolog and its application in AI programming.
This module focuses on Prolog programming, allowing students to gain hands-on experience in building logical models and solving problems using Prolog syntax.
This lecture covers advanced techniques in Prolog, focusing on control mechanisms that enhance program efficiency and effectiveness in AI applications.
This module discusses additional topics in AI, providing insights into emerging trends and techniques that expand the knowledge base beyond traditional AI concepts.
This lecture introduces planning in AI, discussing the importance of planning techniques and strategies in developing intelligent systems that can make informed decisions.
This module focuses on partial order planning, which allows for flexible execution of plans. Students will study its advantages and applications in real-world scenarios.
This lecture covers GraphPLAN and SATPlan, focusing on planning algorithms that utilize graph structures for efficient planning and problem-solving in AI.
This module discusses SATPlan, elaborating on its approach to planning in AI. Students will explore how it formulates planning problems as satisfiability problems.
This lecture introduces reasoning under uncertainty, emphasizing how to make informed decisions when faced with uncertain information in AI applications.
This module covers Bayesian Networks, teaching students how to model uncertain relationships between variables and make probabilistic inferences.
This lecture focuses on reasoning with Bayes Networks, highlighting techniques for inference and decision-making based on Bayesian principles.
This module continues the discussion on reasoning with Bayes Networks, providing advanced techniques and case studies for practical application in AI.
This lecture covers learning techniques focusing on Neural Networks, discussing architectures, training methods, and their applications in various AI domains.
This module discusses issues related to reasoning under uncertainty, emphasizing the challenges faced in AI decision-making processes and potential solutions.
This lecture covers Back Propagation Learning, a fundamental technique in training Neural Networks, explaining the algorithms and processes involved.
This module covers Learning Techniques focusing on Decision Trees, explaining how they can be used for classification and regression tasks in AI.