MACHINE LEARNING AND DATA MINING - 2025/6

Module Overview

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.

Module provider

Computer Science and Electronic Eng

Module Leader

TAMADDONI NEZHAD Alireza (CS & EE)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

COMM054, Data Science Principles and Practices

Module content

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 pattern

Alternative Assessment

N/A

Assessment Strategy

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.

Formative Assessment and Feedback

As formative assessment students will be guided to work on weekly tasks through lab exercises, the solutions to which will provide for feedback on understanding and practice. Labs and feedback will then support the coursework

Module aims

• 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.
  

Learning outcomes

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:

Lectures, to convey and discuss the key concepts and principles,

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.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: COMM055

Other information

Digital Capabilities
The foundational machine learning skills taught in this module provide students digital skills that are fundamental to many computer science problems today. This new set of algorithms allow students to build solutions to a wider class of problems that provide technical skills to work with and extract meaning from large data sets. These are highly employable skills.

Employability
Machine Learning is currently an area that is in high demand in industry. This module teaches both the theory behind cutting edge techniques and the practical skills to develop these systems. students to work with large and complex real world datasets to identify patterns and build models to make predictions on new data. Students are equipped with practical problem-solving skills, theoretical skills, and foundational machine learning skills, all of which are highly valuable to employers.

Global and Cultural Skills
Computer Science is a global language and the tools and languages used on this module can be used internationally. This module allows students to develop skills that will allow them to reason about and develop applications with global reach and collaborate with their peers around the world.

Resourcefulness and Resilience
This module involves practical problem-solving skills that teach a student how to work with complex and unstructured data sets. The algorithms taught in this can be applied to a wide range of different scenarios, giving students new techniques for solving problems.

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 2025/6 academic year.