Module Specifications..
Current Academic Year 2023 - 2024
Please note that this information is subject to change.
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Repeat examination |
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Description This module will provide students with a theoretical and practical grounding in the most important foundational topics in Artificial Intelligence, e.g. AI as search, problem solving, machine learning, machine evolution, perceiving and acting. | |||||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Explain the basic concepts of artificial intelligence, including its philosophical foundations, and present status and future developments. 2. Implement and evaluate search techniques in AI, including constraint satisfaction, heuristic search and adversarial search. 3. Implement and evaluate computational evolution techniques in AI, including genetic algorithms to solve large-dimensional problems. 4. Implement and evaluate machine learning techniques in AI, including some or all of supervised learning, neural networks, knowledge in learning, probabilistic learning, and reinforcement learning. | |||||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and Learning Activities
Introduction to Artificial IntelligenceBrief history of AI, survey of AI main application fields. The state of the art in modern AI including notable applications and successes. Survey of philosophy of AI and its relation to cognitive science. Open issues in AI.Problem solving in AIThe paradigm of AI problem solving as search in a large statespace. Exhaustive search. Heuristic search. Search strategies (informed and non). Games, constraint satisfaction problems.Search - Finding satisfactory pathsDepth-first and breadth-first, iterative deepening, local search and heuristic search. Finding optimal paths: branch and bound, dynamic programming, A*. Adversarial search.Machine evolutionComputational evolution. Genetic algorithms. Codings of genotype. Fitness and reproduction algorithms. Dynamic fitness function. Large dimensional search. Genotype and phenotype.Machine LearningSupervised learning. Neural networks. Back propagation. Coding of inputs. Learning policy. Re-play of experiences. Deep learning. Reinforcement learning. Applications of machine learning. | |||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||||
Programme or List of Programmes
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