Run by School of Computer Science and Electronic Engineering

20 Credits or 10 ECTS Credits

Semester 1

Organiser: Prof Ludmila Kuncheva

To introduce the fundamentals of machine learning and analytics to a non-specialist audience.

Indicative content includes:

• Overview of the structure of the field. Basic principles of machine learning and data analytics.
• Standard and advanced classifiers.
• Clustering and feature selection.
• Elements of data analytics. Descriptive statistics.
• Regression and function approximation.
• Deep Learning neural networks

excellent

Equivalent to the range 70%+. Assemble critically evaluated, relevent areas of knowledge and theory to constuct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.

threshold

Equivalent to 50%. Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.

good

Equivalent to the range 60%-69%. Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.

1. Perform elementary data analysis (descriptive statistics, visualisation, simple regression).

2. Describe the fundamentals of machine learning and data analytics.

3. Apply clustering and feature selection to real-life problems.

4. Discuss the basics of deep learning neural networks and their applications.

5. List basic classification methods and explain their operation.

Type	Name	Description	Weight
COURSEWORK	Assignment 1	Individualised problem set requiring the application of taught theory.	20
COURSEWORK	Assignment 2	Individualised problem set requiring the application of taught theory.	20
EXAM	Exam	End of module unseen examination. A set of questions and problems to solve by hand.	60

		Hours
Lecture	Depending on the requirements, the material will be delivered as full-hour lectures or on-line or lectures of suitable duration. The lectures will be staggered so as to give the students sufficient time to absorb the material. The total timing will be roughly 24 hours of lectures.	24
Practical classes and workshops	Part of the course material will be taught through regular lab sessions held either face-to-face or online. The total time will amount to 24 hours.	24
Private study	Time for the students to revise the material taught in the lectures and the practical sessions, and to prepare for the assessments.	152

• Numeracy - Proficiency in using numbers at appropriate levels of accuracy
• Computer Literacy - Proficiency in using a varied range of computer software
• Exploring - Able to investigate, research and consider alternatives
• Information retrieval - Able to access different and multiple sources of information
• Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.

• Solve problems logically and systematically;
• Knowledge and understanding of facts, concepts, principles & theories
• Problem solving strategies
• Specify, design or construct computer-based systems
• Deploy tools effectively
• Development of general transferable skills
• Methods, techniques and tools for information modelling, management and security
• Defining problems, managing design process and evaluating outcomes
• Knowledge and/or understanding of appropriate scientific and engineering principles
• Knowledge and understanding of mathematical principles
• Knowledge and understanding of computational modelling
• Principles of appropriate supporting engineering and scientific disciplines

Compulsory in courses:

• G5BC: MSc Computing for Data Science year 1 (MSC/CDS)

Optional in courses:

• G4AS: MSc Advanced Computer Science year 1 (MSC/ACS)
• G5BA: MSc Computing year 1 (MSC/COMP)
• G5BD: MSc Rise of the Machines year 1 (MSC/MACH)