- Department: Computer Science
- Module co-ordinator: Dr. Dimitar Kazakov
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2023-24
PADL-M- Probabilistic & Deep Learning
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
- None
Occurrence | Teaching cycle |
---|---|
A | Semester 2 2023-24 |
This module advances the IS stream by introducing the basics of machine learning, purely from an optimisation perspective. The range of topics covers linear regression (picking up where Data left off) to decision trees and a simple neural network (leading into advanced machine learning in the third year). Understanding ML requires knowledge of some mathematical concepts that build upon A-level standard mathematics, specifically: Linear Algebra and Continuous Optimisation. This will be taught in-place. Students will see motivating real world problems, the ML techniques required to solve them, the underlying mathematics needed for the technique and their practical implementation. Practicals will be taught using a python-based modern machine learning library such as TensorFlow or PyTorch and so students will gain experience with the declarative programming paradigm (building on the Software stream).
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 Essay : PADL-M Open Assessment |
N/A | 50 |
Open Examination Open exam : PADL-M VLE Exam |
N/A | 50 |
None
Task | Length | % of module mark |
---|---|---|
Essay/coursework Essay : PADL-M Open Assessment |
N/A | 50 |
Open Examination Open exam : PADL-M VLE Exam |
N/A | 50 |
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