Introductory description

This module offers a holistic exploration into the realms of applied Artificial Intelligence and Data Mining. It is designed to provide a practical understanding of the entire lifecycle of a data mining project, aligning with the CRISP-DM standard methodology and incorporating a range of data analytics tools and techniques. Students will gain hands-on experience in applying and critically evaluating a wide array of machine learning algorithms, including supervised, unsupervised, and reinforcement learning across various datasets. The module also encompasses the use of AI optimisation algorithms, such as Genetic Algorithms (GA) or Particle Swarm Optimization (PSO), for optimisation tasks. Additionally, it delves into cloud-based Machine Learning tools.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Data Science Foundations: Core concepts of Artificial Intelligence, Machine Learning and Data Mining; CRISP-DM methodology, data understanding, data pre-processing & feature engineering, data imbalance handling methods (algorithm level techniques, undersampling, oversampling), hyperparameter optimisation and an introduction to Explainabilty in AI (XAI) .

Unsupervised learning: Clustering Algorithms; Association Rule Mining; Principal Component Analysis.

Supervised learning: Classification, Theoretical background and classification algorithms (e.g. K-Nearest Neighbours, Decision Trees, Support Vector Machines or Random Forest); Regression models(e.g. Linear models, Logistic Regression or Lasso Regression); Ensemble models (e.g. voting, bagging, or boosting), model selection and evaluation.

Reinforcement Learning: Core concepts of Reinforcement Learning and Reinforcement Learning algorithms (e.g., Q-Learning or Policy Optimization methods.)

AI Optimisation Algorithms (e.g. Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) or Simulated Annealing. )

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate a comprehensive and practical understanding of all stages in a data mining project based on the CRISP-DM methodology.
• Select and apply the appropriate Artificial Intelligence and Machine Learning algorithm and other required steps to implement an optimised solution.
• Critically evaluate different data analysis and Machine Learning tools and techniques, to design a solution with the best-suited tools and techniques for a specific data mining/analysis problem.
• Collaboratively design, implement, and present complex data mining/Artificial Intelligence solutions using robust, efficient, and optimised methods.
• Synthesize existing data mining or Artificial Intelligence methodologies to articulate a solution's rationale, methodology and broader implications.

Indicative reading list

Chatterjee, C. 2022, Adaptive machine learning algorithms with Python: solve data analytics and machine learning problems on edge devices, [First]. edn, Apress, New York, NY.

Jo, T. 2021, Machine learning foundations: supervised, unsupervised, and advanced learning, Springer, Cham.

Russell, S.J. & Norvig, P. 2021, Artificial intelligence: a modern approach, Global;Fourth; edn, Pearson, Harlow, United Kingdom.

Sutton, R. S. and Barto, A. G. (2018) Reinforcement learning: an introduction. Second edition. Cambridge, Massachusetts: The MIT Press.

Zollanvari, A. 2023, Machine learning with Python: theory and implementation, Springer, Cham.

View reading list on Talis Aspire

International

Topics are of high international demand

Subject specific skills

• Data science, data mining and AI fundamental concepts,
• Implementation of machine learning and AI models and solutions
• Ability to apply data analysis and machine learning tools and techniques and critically evaluate the outcomes

Transferable skills

• Critical thinking and analytical reasoning.
• Problem-solving in complex and uncertain environments.
• Collaboration and teamwork.
• Effective technical communication.
• Independent learning and self-direction.
• Time and project management.