Intelligent Systems: Probabilistic & Deep Learning - COM00184M

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  • Department: Computer Science
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2025-26

Module summary

PADL-M- Probabilistic & Deep Learning

Related modules

Pre-Requisite knowledge - Understanding of the theory and practice of Machine Learning. For undergraduates this is covered in intelligent systems modules (for example, INT2 -  Intelligent Systems 2: Machine Learning & Optimisation COM00024I). For master's students this is covered in FOAM - Foundations of AI and Machine Learning COM00196M.

 

Module will run

Occurrence Teaching period
A Semester 2 2025-26

Module aims

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).

Module learning outcomes

  • 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 regression, 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

Indicative assessment

Task % of module mark
Essay/coursework 100

Special assessment rules

None

Indicative reassessment

Task % of module mark
Essay/coursework 100

Module feedback

Feedback is provided through work in practical sessions, and after the final assessment as per normal University guidelines

Indicative reading

Solomon, Justin. Numerical Algorithms. AK Peters/CRC Press, 2015