Award in Introduction to Artificial Intelligence (Computer Science)

The Introduction to Artificial Intelligence module provides students with a comprehensive understanding of the foundational principles, concepts, and theories that underlie the field of artificial intelligence (AI). This module is designed to be theoretical in nature, emphasizing the core ideas and theoretical frameworks that drive AI research and development. module, students are expected to: - Develop a deep comprehension of the fundamental concepts and principles of artificial intelligence. - Acquire knowledge of AI's historical context and its evolution. - Understand the core challenges and issues that AI seeks to address. - Define the essential theories and models used in AI, including knowledge representation, reasoning, and machine learning. Analyze the ethical and societal implications of AI technologies.

The module will cover the following topics:

1. Introduction to AI: - Historical perspective on AI. - Definition and scope of AI. - Major AI milestones and achievements.
2. AI Problem Solving: - Search algorithms and problem-solving techniques. - Game playing and adversarial search.
3. Knowledge Representation and Reasoning: - Knowledge representation techniques, including propositional and first-order logic. - Inference and reasoning in AI.
4. Machine Learning Fundamentals: - Overview of machine learning and its types. - Supervised learning, unsupervised learning, and reinforcement learning.
5. Probability and Uncertainty: - Introduction to probability theory. - Bayesian networks and probabilistic reasoning.
6. AI Ethics and Societal Impact: - Ethical considerations in AI research and applications. - Societal implications, bias, fairness, and transparency.
Assessment in this module will take the form of exams, assignments, and project work. The focus will be on evaluating student's understanding of the concepts and techniques of AI, as well as their ability to apply these concepts and techniques to real-world problems.