AI Systems Implementation - CO826

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2021 to 2022
Spring 7 15 (7.5) checkmark-circle


Students are presented during lectures with advanced Artificial Intelligence/Machine Learning techniques (such as genetic algorithms, support vector machines (SVMs), deep learning, neural networks, stochastic gradient decent, Q-Learning/Deep Q-learning, ensembles, neuroevolution), including aspects of implementation, hyper parameter tuning, scalability and parallelism.


Contact hours

Contact hours: 31
Hours of private study: 119
Total hours 150

Method of assessment

This module will be assessed by 100% coursework.

Indicative reading

• Patrick D. Smith, "Hands-On Artificial Intelligence for Beginners: An introduction to AI concepts, algorithms, and their implementation", Packt Publishing, 2018
• M. Tim Jones, "Artificial Intelligence: A Systems Approach", Jones & Bartlett Learning, 2015
• C. Aggarwal. Neural Networks and Deep Learning: a textbook. Springer, 2018.
• Aurélien Géron, “Hands-on Machine Learning with Scikit-Learn and TensorFlow”, O'Reilly, 2017
• Sebastian Raschka, “Python Machine Learning”, 2nd Ed, Packt Publishing, 2017
• Ian Goodfellow, Yoshua Bengio and Aaron Courville. “Deep Learning”, MIT Press, 2016
• Sarah Guido, Andreas C. Müller, “Introduction to Machine Learning with Python”, O'Reilly, 2016
• Tom Mitchell, “Machine Learning”, McGraw Hill, 1997

Learning outcomes

1. Demonstrate a systematic understanding of techniques used to implement AI Systems, and their underpinning principles;
2. Evaluate critically alternative AI approaches according to quality/cost trade-offs;
3. Master the application of AI techniques to solve realistic and real-world problems, using appropriate programming languages and libraries (e.g., Python and Scikit-Learn);
4. Analyse and critically evaluate the obtained results in terms of quality/cost, and devise strategies to improve or replace them.


  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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