This course offers a comprehensive overview of the field of Artificial Intelligence (AI), covering its fundamental concepts, techniques, and applications. The topics include:

• Problem Solving by Searching: Understanding various search strategies used to navigate complex problem spaces, including uninformed search methods like breadth-first and depth-first search, as well as informed methods such as A* and greedy search.
• Constraint Satisfaction Problems (CSPs): Exploring techniques to solve problems defined by constraints, such as scheduling, map coloring, and Sudoku, using methods like backtracking and constraint propagation.
• Local Search Algorithms: Delving into optimization techniques that operate in the space of potential solutions, such as hill-climbing, simulated annealing, and other heuristic-based approaches.
• Genetic Algorithms: An introduction to evolutionary algorithms inspired by natural selection, used to solve optimization and search problems by evolving solutions over generations.
• Introduction to Neural Networks: Examining the basics of artificial neural networks, their structure, and how they are trained for tasks like classification and regression, with a focus on foundational models such as perceptrons and multi-layer networks.
• Knowledge Representation and Propositional Inference: Studying methods to represent knowledge in a form that machines can process, such as logic-based representations, and applying propositional inference techniques to draw conclusions from given knowledge bases.

By the end of this course, students will have gained foundational knowledge of AI concepts and be equipped to apply these techniques to solve real-world problems or pursue more advanced studies in the field.