CS607 Artificial Intelligence

CS607: Artificial Intelligence

CS607 Artificial Intelligence (AI) provides a broad introduction to the exciting and rapidly evolving field of building machines that can think and act intelligently. This course explores the foundational concepts, techniques, and algorithms that enable computers to mimic human cognitive functions like problem-solving, learning, reasoning, and perception.

The course is structured around the concept of an "intelligent agent"—an entity that perceives its environment and takes actions to maximize its chances of success. You will study a wide array of methods that agents use to solve complex problems, from searching for solutions in vast state spaces to reasoning under uncertainty. This course provides a comprehensive overview of "classic AI" as well as an introduction to modern machine learning.

Key Topics Covered:

• Foundations and Agents: The history of AI, the different types of AI (narrow vs. general), and the concept of rational intelligent agents (PEAS framework).
• Problem Solving and Search: How to frame problems for an AI. This includes uninformed search algorithms (like Breadth-First Search and Depth-First Search) and, more importantly, informed (heuristic) search algorithms (like A* Search and Greedy Best-First Search).
• Adversarial Search: The techniques used to build AIs for two-player games (like chess or tic-tac-toe), including the Minimax algorithm and Alpha-Beta Pruning.
• Knowledge Representation and Reasoning: How to represent 'what an AI knows' in a formal way. This includes propositional logic, first-order logic, and inference mechanisms like forward and backward chaining. This is the core of Expert Systems.
• Handling Uncertainty: Introduction to probability, Bayesian networks, and fuzzy logic to allow agents to reason in environments that are not fully predictable.
• Machine Learning: An introduction to the field of AI where systems learn from data. This includes:
  • Supervised Learning: Learning from labeled data (e.g., decision trees, linear regression).
  • Unsupervised Learning: Finding patterns in unlabeled data (e.g., clustering with k-means).
• Advanced Topics: A brief look at emerging fields like Natural Language Processing (NLP), Computer Vision, and Robotics.

Course Objectives:

1. Understand the fundamental concepts and challenges of Artificial Intelligence.
2. Apply and implement various search algorithms (uninformed, informed, adversarial) to solve well-defined problems.
3. Use logic-based systems for knowledge representation and reasoning.
4. Grasp the basic principles of machine learning, including supervised and unsupervised learning.
5. Appreciate the breadth of AI applications and their impact on society.

CS607 is a gateway to one of the most dynamic areas of computer science. It provides the necessary foundation for advanced courses in machine learning, data science, robotics, and natural language processing.