This unit covers the history of artificial intelligence and the foundational concepts of intelligent agents. It delves into problem-solving and search techniques, including problem representation, heuristic search, and adversarial search. You will learn about knowledge representation and reasoning, focusing on propositional and first-order logic for AI applications, as well as planning. The unit also explores reasoning under uncertainty through Bayesian Networks and Markov Decision Processes. In the realm of machine learning, the unit includes reinforcement learning techniques, supervised learning such as decision trees, Naive Bayes, neural networks, and self-supervised learning approaches. Additionally, the unit addresses various AI applications and examines ethical considerations in AI
Applied sessions start from Week 2. Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.
Explain, apply and evaluate the goals of AI and the main paradigms for achieving them including logical inference, search, machine learning and Bayesian inference;
Describe, analyse, apply and evaluate heuristic AI for problem solving;
Explain the and understand the practical and ethical implications of Artificial Intelligence in real world contexts.
Describe the historical and conceptual development of AI
Describe, analyse, apply and evaluate machine learning techniques;
Describe, analyse and apply probabilistic inference mechanisms for reasoning under uncertainty;
Describe, analyse and apply basic knowledge representation and reasoning mechanisms;
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