This Artificial Intelligence full university course is designed to teach the fundamental principles and algorithms that form the foundation of modern AI systems. It provides a structured academic approach covering reasoning, search, constraints, and machine learning.
The course begins with an introduction to artificial intelligence and its scope, helping learners understand how AI systems are designed to solve complex problems. It then explores reasoning techniques such as goal-based reasoning, goal trees, and rule-based expert systems.
A major part of the course focuses on search algorithms, including depth-first search, hill climbing, beam search, optimal search methods, branch and bound, and A* search. These techniques are essential for solving optimization and pathfinding problems in AI.
Students will also learn game-playing strategies such as minimax and alpha-beta pruning, which are widely used in competitive AI systems.
The course then introduces constraint satisfaction problems, including visual interpretation, search reduction techniques, and object recognition through constraints. These concepts help in understanding structured problem-solving in AI.
In the learning section, students explore nearest neighbors, decision trees, and disorder-based learning methods. The course concludes with an introduction to neural networks, providing foundational knowledge of deep learning systems.
By the end of this course, learners will have a strong theoretical understanding of artificial intelligence, including search, reasoning, constraints, learning models, and neural networks.
