MACHINE LEARNING FOR DATA SCIENCE - 2026/7
Module code: COMM075
Module Overview
Machine Learning for Data Science incorporates a wide range of machine learning algorithms and data mining techniques, which can be applied to real-world problems and datasets with various characteristics to generate new insights and understanding. Through treatment of the principles and fundamental requirements for machine learning, example applications, and related exercises, this module will offer coverage of a range of contemporarily important and emergent machine learning 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
Overall student workload
Independent Learning Hours: 78
Lecture Hours: 22
Laboratory Hours: 22
Guided Learning: 6
Captured Content: 22
Module Availability
Semester 2
Prerequisites / Co-requisites
N/A
Module content
This module covers a wide range of machine learning algorithms and data mining techniques for data science. Weekly lectures introduce the theoretical foundations as well as the practical implications for various representative algorithms from the main machine learning techniques, followed by weekly lab sessions for hands-on practice. Indicative topics include decision trees, neural networks, Bayesian learning, instance-based learning, support vector machines, clustering, computational learning theory and PAC learning, relational and logic-based learning, and reinforcement learning. Appropriate software tools will also be introduced for exploring these techniques in the lab.
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| Coursework | Group Coursework | 100 |
Alternative Assessment
Individual Coursework
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 group 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 12.
Module aims
- Introduction to Machine Learning (ML) and Data Mining (DM) for Data Scientists
- To elaborate and demonstrate a variety of ML algorithms and DM approaches for the treatment of datasets of various kind
- Applications of ML/DM through case studies and practical examples in lab sessions and coursework
Learning outcomes
| Attributes Developed | Ref | ||
|---|---|---|---|
| 001 | Select the most appropriate ML/DM techniques for a given problem and provide a well-reasoned rationale for the choice of solution | KCPT | LODA.02 L2 |
| 002 | Understand and effectively use variety of ML algorithms (supervised, unsupervised and semi-supervised) and DM techniques (through the whole DM lifecycle) | CPT | DSDA01 |
| 003 | Discuss the benefits / limitations of different approaches and evaluate and compare them using different performance measures on different type of data (inc complex datasets) | CPT | LODA.03 L2 |
| 004 | Design and evaluate ML/DM techniques and tools to discover new relations and insights for a given problem | KCPT | LODA.01 L3 |
Attributes Developed
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:
- 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 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: COMM075
Other information
The school of Computer Science and Electronic Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas: 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 capabilities: 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: 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. Sustainability: To identify global environmental impacts of technology solutions and how they are to be designed or deployed in a way that will sustain long term. |
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 2026/7 academic year.