- Department: Computer Science
- Module co-ordinator: Dr. Dimitar Kazakov
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2022-23
- See module specification for other years: 2021-22
This module looks at modern applications of neural networks and machine learning. The practical elements of the course will focus on implementing a range of systems to handle real-world problems and evaluating the results appropriately.
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
- None
Students on the joint Computer Science and Mathematics courses who are interested in this module should first discuss the necessary prerequisite knowledge with the module leader.
Occurrence | Teaching period |
---|---|
A | Autumn Term 2022-23 |
This module follows on from Intelligent Systems 2, and looks at modern applications of neural networks and machine learning. The practical elements of the course will focus on implementing a range of systems to handle real-world problems and evaluating the results appropriately.
Apply the probabilistic basis of machine learning to problems
Demonstrate a working knowledge of manifold embedding and kernel methods
Apply a range of Bayesian methods for classification and clustering
Be familiar with the main deep learning architectures
Use the optimisation process and apply different variants (i.e. gradient descent, stochastic algorithms, ADAM)
Work on systems that handle larger amounts of training data and be able to discuss the problems and solutions
Task | Length | % of module mark |
---|---|---|
Essay/coursework Intelligent Systems 3 |
N/A | 100 |
None
Task | Length | % of module mark |
---|---|---|
Essay/coursework Intelligent Systems 3 |
N/A | 100 |
Feedback is provided through work in practical sessions, and after the final assessment as per normal University guidelines.
Solomon, Justin. Numerical Algorithms. AK Peters/CRC Press, 2015