MACHINE LEARNING AND DATA MINING - 2023/4
Module code: COMM055
Machine Learning and Data Mining incorporate a large number of ways in which datasets with a variety of characteristics can be processed in order to provide for new insights and understanding. Through treatment of the principles and fundamental requirements for ML/DM, example applications, and related exercises, this module will offer coverage of a range of contemporarily important and emergent data treatments and algorithms. The module will provide for the means to critically evaluate, extend, and apply, appropriate techniques to datasets exemplifying specific characteristics in order to derive suitable and defensible results.
TAMADDONI NEZHAD Alireza (Elec Elec En)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: I400
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 95
Tutorial Hours: 11
Laboratory Hours: 22
Captured Content: 22
Prerequisites / Co-requisites
COMM054, Data Science Principles and Practices
Indicative content includes: (Data Mining EDISON descriptors KU1.01.02-KU1.01.05, KU1.01.08, KU1.03.01, KU1.03.04, KU1.03.05, KU1.03.06, KU1.04.02, KU1.02.01- KU1.02.05, KU1.02.08, KU1.05.01)
• Introduction to Machine Learning and Data Mining for Data Science
• Decision Tree Learning
• Neural Networks
• Bayesian Learning
• Instance-based Learning and Support Vector Machines (SVMs)
• Clustering and Dimensionality Reduction
• Computational Learning Theory and PAC Learning
• Relational Learning and Inductive Logic Programming (ILP)
• Inductive vs Abductive Learning and Knowledge Discovery
• Meta-Interpretive Learning (MIL), Predicate Invention and Grammar Learning
• Reinforcement Learning, Model-free prediction & control, Q-learning
• Evolutionary Machine Learning and Learning Classifier Systems (LCS)
• Appropriate software will be used for exploration of the above in the lab.
|Assessment type||Unit of assessment||Weighting|
The assessment strategy is designed to provide students with the opportunity to demonstrate the ability to critically evaluate, extend, and apply, appropriate techniques to datasets exemplifying specific characteristics in order to derive suitable and defensible results.
Thus, the summative assessment for this module consists of:
• A coursework involving a report comprising the comparative evaluation of machine learning and data mining methods when applied and adapted, using common software tools to specified datasets, with documentation of key insights derived. This will evaluate LOs 1, 2, 3, 4 and have a deadline in or near to week 11.
- This module aims to:
elaborate, demonstrate, and apply a variety of machine learning and data mining approaches for the treatment of datasets of various kinds, and in ways which may sometimes be considered artificially intelligent.
|001||On successful completion of this module, students will be able to: Select most appropriate statistical techniques and model available data to deliver insights (LODA.02 L2)||CKPT|
|002||Effectively use variety of data analytics techniques, from Machine Learning (including supervised, unsupervised, semi-supervised learning) and Data Mining for complex data analysis through the whole data lifecycle (DSDA01 [refined])||CPT|
|003||Analyze available data sources and define tools that work with complex datasets (LODA.03 L2 [refine])||CPT|
|004||Design and evaluate analysis techniques and tools to discover new relations (LODA.01 L3)||CKPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to provide students with the knowledge, skills, and practical experience covering the module aims and learning outcomes.
The learning and teaching methods include: 11 teaching weeks with each week comprising:
2 hour lectures, to convey and discuss the key concepts and principles
2 hour lab sessions, to put key concepts and principles into practice
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: COMM055
Programmes this module appears in
|Data Science MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.