Rhedir gan School of Computer Science and Electronic Engineering

20.000 Credyd neu 10.000 Credyd ECTS

Semester 2

To extend the student's knowledge of machine learning acquired through module ICE3701 (Machine Learning). To introduce them to modern-day challenges and solutions. Challenges include imbalanced data, streaming data and concept drift. Solutions include Classifier ensembles and Deep Learning neural networks.

Indicative content includes:

• Review of the fundamentals of machine learning: supervised and unsupervised learning, classifier models, feature selection.
• Classifier ensembles.
• Imbalanced data, streaming data and concept drift: machine learning solutions.
• Introduction to Deep Learning neural networks and their applications.

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.

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.

1. Apply (by hand) some of the learned methods to toy problems.

2. Understand the challenges of modern data and the potential of machine learning to address them.

3. Apply some of the learned methods to solve toy problems and real-life problems.

4. Demonstrate knowledge of advanced machine learning methods at an algorithmic level.

Math	Enw	Disgrifiad	Pwysau
	Class Test		20.00
	Lab report		20.00
	Lab report		60.00

		Oriau
Laboratory	2 hours of labs x 12 weeks.	24
Lecture	2 hours of lectures x 12 weeks	24
Private study	The students will revise the material given in the lectures, prepare for the class test, and prepare the lab reports.	152

• 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

• Solve problems logically and systematically;
• Access and synthesize information and literature sources;
• Analyse and display data using appropriate methods and mathematical techniques;
• Demonstrate familiarity with relevant subject specific and general computer software packages.
• Knowledge and understanding of facts, concepts, principles & theories
• Use of such knowledge in modelling and design
• Problem solving strategies
• Knowledge and understanding of mathematical principles

Goblygiadau o ran adnoddau ar gyfer myfyrwyr

N/A

Rhestr ddarllen

N/A

Gorfodol mewn cyrsiau:

• H118: BSc Data Science & Artificial Intelligencetellig year 3 (BSC/DSAI)
• H113: BSc Data Science and Machine Learning year 3 (BSC/DSML)