module specification

This module provides an appreciation of data mining and machine learning fundamental concepts, algorithms, and process. It covers machine learning algorithms and data mining techniques for data analysis, pattern mining, clustering, classification and regression. It equips the students with practical skills in applying data mining and machine learning techniques in real-world analytics 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.

• 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 to classify: Decision Tree, Naïve Bayes, Bayesian networks, Support Vector Machines [LO3],[LO4],[LO6]
•  Machine learning to predict: Logistic regression, Neural network, and Deep Learning [LO3],[LO4],[LO5],[LO6]
• Mining relationships among records: cluster analysis,  association analysis (‘market basket analysis’) [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 Identify machine learning algorithms appropriate for particular classes of problems
LO4 Undertake a comparative evaluation of the strengths and limitations of various data mining techniques
LO5 Comprehensive understanding of the state of the art techniques in data mining and machine learning
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/15BCF94D-F01E-AD5A-FE85-619EE204ECA7.html?lang=en-GB

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