This module covers the basic principles of machine learning and the kinds of problems that can be solved by such techniques. Students will learn about the philosophy of AI, how knowledge is represented and algorithms to search state spaces. The module also provides an introduction to both machine learning and biologically inspired computation.
Total contact hours: 22
Private study hours:128
Total study hours: 150
Method of assessment
Main assessment methods
A1 – Practical assignment (25%)
A2 – Practical assignment (25%)
2 hour unseen written examination (50%)
S.J. Russell & P. Norvig, "Artificial Intelligence: a modern approach", 4th Edition. Pearson, 2020. (main textbook)
S. Pinker. "How the Mind Works", W.W. Norton & Company, 1999.
A. Cawsey, “The Essence of Artificial Intelligence”, Prentice-Hall, 1998.
P. Bentley. “Digital Biology: How Nature Is Transforming Our Technology and Our Lives”, Simon & Schuster, 2007.
S. Marsland, "Machine Learning: An Algorithmic Perspective", CRC Press, Taylor and Francis, 2nd Edition, 2014.
E. K. Burke & G. Kendall, "Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques", 2nd Edition, Springer, 2014
S. Haykin, “Neural Networks and Learning Machines”, 3rd Edition. Pearson, 2009
1 Demonstrate knowledge and understanding of the motivation for designing intelligent machines, their implications and associated philosophical issues, such as the nature
of intelligence and learning.
2 Demonstrate systematic understanding and critical awareness of the main kinds of state-space search algorithms, discussing their strengths and limitations.
3 Understand and explain the main concepts and principles associated with different kinds of knowledge representation, such as logic, case-based representations, and
4 Understand and explain the differences between the major kinds of machine learning problems – namely supervised learning, unsupervised learning and reinforcement
learning – and describe the fundamental ideas of algorithms for solving those problems.
5 Demonstrate mastery of the main concepts and principles of major kinds of biologically-inspired algorithms, and understand what is required in order to implement and
evaluate one such technique.
6 Demonstrate comprehensive understanding of how various intelligent-system techniques have been used in the context of several case studies, and critically compare
different techniques in the context of those case studies.
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Credit level 7. Undergraduate or postgraduate masters level module.
- ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
- The named convenor is the convenor for the current academic session.
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