1. Know and be able to articulate the differences between artificial intelligence and machine learning, and to be able to define and give examples of different machine learning techniques.
2. Understand the operation of key algorithms used for machine learning, data classification, and applying Artificial Neural Networks (ANN).
3. Understand how evolutionary algorithms are used in real-world applications.
4. Implement different algorithms using a programming language.

Essay (2,500 words)
You are required to choose an aspect of machine learning that you have studied on this module, for example (but not limited to) artificial neural networks, Bayesian classification, computer vision or the ethical dimensions of AI and write a related essay.
(Learning Outcomes 1 to 3).
Practical Demonstration – Report (1,000 words)
Using a programming language or tool and a sample dataset provided you will model solutions to scenarios (Learning Outcome 4).

This module addresses the following topics:
-Philosophical foundations: Artificial Intelligence (AI) vs. Machine Learning (ML)
-Learning paradigms: Supervised learning, unsupervised learning and reinforcement learning
-Real-life applications of ML – case studies
-The principles of an Artificial Neural Network (ANN)
-Statistical principles: Correlation and regression
-Classification techniques: Bayes Theorem, Naďve Bayes and K-means clustering
-Principles of evolutionary/genetic algorithms

In this module, you will learn some fundamental mathematical principles of artificial intelligence and machine learning, and learn and practice several techniques for building and testing machine learning models. You will also learn about the principles of an Artificial Neural Network and be able to articulate the operation of the fundamental components and how they interact. You will learn about evolutionary algorithms, techniques for building adaptive and self-learning ML models. You will also learn about how machine learning can be applied to search for optimal solutions using multiple agents.

Face-to-face/Online class-based sessions (18 hours)
There are 18 hours of class-based teaching delivery presented face-to-face or online, which will include lectures, practical demonstrations and group work where appropriate.

Assessment Clinic (2 hours)
There are 2 hours of face-to-face/online teaching aimed at helping you complete your assignment. This will include both classroom-led guidance and an allocation of time for one-to-one support with your tutor.

Self-led Learning (180 hours)
You are expected to spend 180 hours in self-led learning. This includes working through supplied tutorials, tools practice and background/guided reading.

Recommended (not essential):
Stanford, M. (2020), The Age of AI: An Introduction to Big Data, Machine Learning, and Neural Networks, Randall Press, ASIN:B084R9SQ5H
Stone, J. (2020), A Brief Guide to Artificial Intelligence (Tutorial Introductions), Sebtel Press, ASIN:B086L99WZ8
Mishra, P. (2021), Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks, Apress, ASIN:B09PJB8MJC
Rothman, D. (2020), Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps, Packt Publishing, ISBN-10:1800208138
Gianfagna, L. and Di Cecco, A. (2021), Explainable AI with Python, Springer 1st Edition, ASIN:B093S1PMWR
Frankish, K. and Ramsey, W.M. eds., 2014. The Cambridge handbook of artificial intelligence. Cambridge University Press.
Zambrano-Bigiarini, M.; Clerc, M.; Rojas, R. (2013). Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements. Evolutionary Computation (CEC), 2013 IEEE Congress on. pp. 2337–2344.
Ant Colony Optimization by Marco Dorigo and Thomas Stützle, MIT Press, 2004. ISBN 0-262-04219-3
Bramer, M.A. (2016) Principles of data mining. 3rd edn. London: Springer.
ISBN:1447173066 9781447173069
Witten, I.H., Frank, E. and Hall, M. (2011) Data mining: practical machine learning tools and techniques. 3rd edn. London; Amsterdam: Morgan Kaufmann. EAN: 9780080890364
Raschka, S. (2017) Python machine learning: machine learning and deep learning with Python, scikit-learn, and TensorFlow. EAN: 9781787126022

Other background / recommended reading will be indicated in the module contents.

You will need access to these resources:
1. Blackboard VLE for module information and learning materials
2. Microsoft Teams for module communication
3. Staffordshire University library access (physical or digital) for access to recommended texts
4. It is strongly recommended that you have your own laptop to develop practical aspects of the module