Machine Learning 2022-23
School Of Computer Science And Electronic Engineering
Module - Semester 1
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.
- Neural networks: standard architectures and deep learning.
-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. -excellent -Equivalent to the range 70%+. Assemble critically evaluated, relevant areas of knowledge and theory to construct 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.
2-hour exam of type "Choose any 2 of 4", consisting of problems to solve by hand. The problems will be similar to those in the labs and assignments. All notes, books, and internet resources are permitted.
A collection of small problems based on the second half of the module. Hand-crafted solutions and short Python code solutions are expected.
A collection of small problems based on the first half of the module. Hand-crafted solutions and short Python code solutions are expected.