Advanced Machine Learning
Rhedir gan School of Computer Science and Electronic Engineering
20.000 Credyd neu 10.000 Credyd ECTS
Trefnydd: Prof Ludmila Kuncheva
To extend the student's knowledge of machine learning acquired through module ICE-3083 (Pattern Recognition and Neural Networks). 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.
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.
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.
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.
Understand the challenges of modern data and the potential of machine learning to address them.
Demonstrate knowledge of advanced machine learning methods at an algorithmic level.
Compose thoughts and findings in a clear, scientific manner.
Apply (by hand) some of the learned methods to toy problems.
Program some of the learned methods to solve toy and real-world problems.
Strategaeth addysgu a dysgu
The students will revise the material given in the lectures, prepare for the class test, prepare the lab report and write the final article.
2 hours of labs x 12 weeks.
2 hours of lectures x 12 weeks
- Llythrennedd - Medrusrwydd mewn darllen ac ysgrifennu drwy amrywiaeth o gyfryngau
- 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
Sgiliau pwnc penodol
- 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