module specification

This module provides an appreciation of data mining and machine learning fundamental concepts, algorithms, and process. It explores contemporary machine learning and data mining methodologies used for knowledge discovery and predictive analytics. The module covers key concepts and techniques for pattern recognition, clustering, classification, regression, and other data-driven machine learning approaches, enabling students to apply state-of-the-art tools/frameworks to real-world analytical problems.

The aims of this module are to:
· provide students with an understanding of data mining and machine learning fundamental concepts, algorithms, and process.
· understand the purpose and breadth of areas of application of data mining and machine learning
· understand and compare the techniques and tools available for various type of data analytics problems
· develop students with practical skills in applying data mining techniques to solve real-world analytics problems.

N/A

· Concepts and fundamentals of data mining and machine learning [LO1], [LO2]
· Data mining process: Cross Industry standard processing (CRISP) for data mining
· [LO1]
· Data preparation and graphical exploration: visualising large data sets, data cleaning, outlier detection, variable transformation [LO1], [LO3]
· Machine learning for classification, focusing on principles, methodologies, and evaluation of classification models such as linear regression, text and image classification [LO3], [LO4], [LO6]
· Machine learning for prediction, emphasizing predictive modelling techniques, including state-of-the-art approaches such as Neural networks and deep learning, and their applications across diverse domains [LO3], [LO4], [LO5], [LO6]
· Mining relationships and structures within data, including techniques for identifying associations, clusters, and hidden patterns [LO3], [LO4], [LO6]
· Model evaluation and predictive performance [LO4], [LO5], [LO6]

Topics will be introduced through the medium of formal lectures, supported by tutorial and workshop sessions, and blended learning as follows:

- Lecture (2 hour / week): Introduction of the major topics identified in the syllabus, plus practical exercises, directed reading and other further studies

- Workshop (2 hour / week): Data mining technical skills will be further developed through lab-based workshops. Specific practical exercises are set to support students' development of skills with powerful mining package (e.g. Python Scikit Learn and TensorFlow).

- Blended learning: Using the University’s VLE and online tools to deliver content, assessment and feedback, to encourage active learning, and to enhance student engagement and learning experience.

On successful completion of this module the student should be able to:
[LO1] Demonstrate a comprehensive understanding of data mining and machine learning fundamental concepts, algorithms and process
[LO2] Demonstrate an understanding of the purpose and breadth of areas of application of data mining and machine learning
[LO3] Critically assess and select appropriate machine learning algorithms and data mining approaches for specific analytical tasks or problem domains.
[LO4] Undertake a comparative evaluation of the strengths and limitations of various data mining techniques
[LO5] Demonstrate an understanding of current advancements, tools, and state-of-the-art techniques in data mining and machine learning, and their practical applications.
[LO6] Demonstrate capacity to perform a self-directed piece of practical work that applies data mining techniques in a real-world problem and considers potential commercial risk.

Reading list available at: https://rl.talis.com/3/londonmet/lists/9972BFE8-D3A2-C713-3EA8-C341529756D7.html?lang=en-GB&login=1

1. Géron, A., (2023). Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. 3rd ed. Sebastopol, CA: O’Reilly Media.
2. Lindholm, A., (2022). Machine Learning: A First Course for Engineers and Scientists. Cambridge: Cambridge University Press.
3. A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth,(2020) Mathematics for Machine Learning, Cambridge University Press
4. Aurélien Géron (2019) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly Media [Core]
5. Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal (2016) Data Mining: Practical Machine Learning Tools and Techniques, Elsevier Science
6. Mohammed J. Zaki and Wagner Meira, Jr., (2020) Data Mining and Machine Learning: Fundamental Concepts and Algorithms, 2nd ed. Cambridge University Press [Core] 7. Xin-She Yang, (2019) Introduction to Algorithms for Data Mining and Machine Learning, Elsevier Science