Machine Learning (994G5)
Machine Learning
Module 994G5
Module details for 2025/26.
15 credits
FHEQ Level 7 (Masters)
Module Outline
This module will allow you to be able to implement, develop and deploy Machine Learning techniques to real-world problems. In order to take this module, apprentices need to have already taken the 'Mathematics for Data Analysis’ module.
Indicative Content
• Probabilistic and non-probabilistic classification and regression methods.
• Reinforcement learning approaches including the non-linear variants using kernel methods.
• Techniques for pre-processing data (including Principle Component Analysis).
Module learning outcomes
Identify the strengths and weaknesses of state-of-the-art supervised, unsupervised, and reinforcement machine learning models including multi-layer perceptron, support vector machine, random forest, K-means, PCA, and Q-learning.
Critically analyse and implement several stochastic optimization methods ranging from stochastic gradient descent, stochastic variance reduction, to adaptive gradient methods for training machine learning models on big data.
Critically demonstrate knowledge of the fundamental principles of advanced machine learning models including probabilistic graphical models and statistical network models.
Systematically apply developed classification/regression techniques with stochastic optimization to real-world problems, including extracting deep convolutional neural network features and incorporating prior knowledge.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Project | T1 Week 11 | 100.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
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