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An increasing number of businesses will adopt Artificial Intelligence (AI) to enhance their decision-making processes. No doubt, AI will have further transformative, far-reaching implications for society that requires close attention. The primary objective of this course is to provide a basic understanding of the AI concepts and their implications. The course will introduce the basic principles, techniques, and applications of AI. Emphasis will be placed on teaching the concepts, not providing mastery of particular software tools or programming environments. Coverage includes knowledge representation, intelligent agents, AI applications, problem-solving, search algorithms, machine learning, deep learning, reinforcement learning, robotics in AI, and AI ethics.
After successful completion of this course students are expected to be able to:
- explain different types of AI agents;
- identify the benefits of using AI-based systems;
- recognize various AI search algorithms, e.g., uninformed, informed, heuristic, constraint satisfaction;
- compare the fundamentals of knowledge representation, e.g., logic-based, semantic networks, inference;
- identify key machine learning approaches and algorithms;
- identify reinforcement learning;
- know how to build simple knowledge-based systems;
- identify neural networks and deep learning techniques;
- outline different robotic systems and applications;
- recognize various ethics and governance to ensure successful AI project delivery.