Modiwl ICE-3701:
Machine Learning

Rhedir gan School of Computer Science and Electronic Engineering

20 Credyd neu 10 Credyd ECTS

Semester 1

To introduce the fundamentals of machine learning which include basic and advanced classification methods, clustering and feature selection. To enable the students to apply some of the learned methods to real data sets.

Indicative content includes:

• Basics of machine learning: Concepts of object, class, feature. Training and testing protocols. Error estimation. ROC curves. Supervised and unsupervised learning.
• Classification methods: basic classifiers and classifier ensembles.
• Feature selection.
• Clustering.
• Neural networks: standard architectures and deep learning.

da

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.

ardderchog

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.

trothwy

Equivalent to 40%. 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.

1. Explain and apply the basic notions and principles of machine learning.

2. Summarise neural network models and their training procedures.

3. Apply feature selection methods with different classifiers.

4. Compare and contrast different classifier models and their operation.

5. Detail and apply clustering algorithms to data sets.

Math	Enw	Disgrifiad	Pwysau
GWAITH CWRS	Assignment 2	Solve a set of 5-6 problems.	20
ARHOLIAD	Examination	End of semester final unseen examination.	60
GWAITH CWRS	Assignment 1	Solve a set of 5-6 problems.	20

		Oriau
Lecture	2 lectures per week x 12 weeks	24
Laboratory	24 hours over 12 weeks (2 hours per week) including 72 hours for preparation.	96
Private study	Self-study. Revision after the lectures. Preparation for the exam and writing the assignments.	80

• Rhifedd - Medrusrwydd wrth ddefnyddio rhifau ar lefelau priodol o gywirdeb
• Defnyddio cyfrifiaduron - Medrusrwydd wrth ddefnyddio ystod o feddalwedd cyfrifiadurol
• Hunanreolaeth - Gallu gweithio mewn ffordd effeithlon, prydlon a threfnus. Gallu edrych ar ganlyniadau tasgau a digwyddiadau, a barnu lefelau o ansawdd a phwysigrwydd
• Archwilio - Gallu ymchwilio ac ystyried dewisiadau eraill
• Adalw gwybodaeth - Gallu mynd at wahanol ac amrywiol ffynonellau gwybodaeth
• Dadansoddi Beirniadol & Datrys Problem - Gallu dadelfennu a dadansoddi problemau neu sefyllfaoedd cymhleth. Gallu canfod atebion i broblemau drwy ddadansoddiadau ac archwilio posibiliadau
• Cyflwyniad - Gallu cyflwyno gwybodaeth ac esboniadau yn glir i gynulleidfa. Trwy gyfryngau ysgrifenedig neu ar lafar yn glir a hyderus.

• Knowledge and understanding of facts, concepts, principles & theories
• Use of such knowledge in modelling and design
• Problem solving strategies
• Development of general transferable skills
• Methods, techniques and tools for information modelling, management and security
• Knowledge and understanding of mathematical principles
• Knowledge and understanding of computational modelling

Goblygiadau o ran adnoddau ar gyfer myfyrwyr

N/A

Rhestr ddarllen

N/A

Gorfodol mewn cyrsiau:

• H116: BSc Applied Data Science (Degree Apprenticeship) year 3 (BSC/ADS)
• H113: BSc Data Science and Machine Learning year 3 (BSC/DSML)