Module Information
Module Identifier
CSM6420
Module Title
Machine Learning for Intelligent Systems
Academic Year
2025/2026
Co-ordinator
Semester
Semester 2
Reading List
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Written assignment contrasting the use of several methods discussed in the course, applied to data provided by the lecturers (4000 words). | 60% |
| Semester Assessment | Written analysis of scientific paper(s) (3000 words limit), followed by an oral presentation and discussion on the same. | 40% |
| Supplementary Assessment | Resubmission of failed/nonsubmitted components or others of equivalent value. | 100% |
Learning Outcomes
On successful completion of this module students should be able to:
Demonstrate competence with the machine learning methods and tools considered in this scheme.
Show proficiency in analysing data sets using the appropriate tools.
Demonstrate skills in designing, running and documenting experiments using machine learning.
Demonstrate capability to write and present a detailed analysis of an application of machine learning.
Brief description
This module will equip students with the main concepts in Machine Learning by engaging them in seminar-based discussions on scientific papers. It will then help the students build towards a term paper, which will describe their practical investigation of the issues involved in applying two machine learning methods to an appropriate data set that they will have found.
Content
Introduction to machine learning
- Basic concepts and assumptions
Supervised learning
- Decision Trees, Overfitting
- Naive Bayes and Bayesian Networks, Bayesian decision theory
- K Nearest Neighbours
- Linear models: Linear Regression, Logistic Regression
- Support Vector machines, kernel trick
- Ensemble methods: Bias-variance tradeoff, Boosting, Bagging, Random Forests
Unsupervised learning
- Principal Component Analysis
- Clustering: Hierarchical clustering, K-Means
- Expectation Maximisation, Gaussian Mixture Models
Neural networks and deep learning
- Multilayer Perceptrons, Stochastic Gradient Decent, Backpropagation
- Regularisation methods: L1 and L2 regularisation, Dropout, data augmentation
- Convolutional neural networks
- Recurrent Neural Networks, Long Short-Term Memory
- Autoencoders, Embeddings
- Generative Adversarial Networks
- Transfer learning
Practical methodology
- Data preprocessing, Feature extraction and selection
- Performance evaluation, Hyperparameter tuning
Other topics and in machine learning
Reinforcement Learning
- Basic concepts and assumptions
Supervised learning
- Decision Trees, Overfitting
- Naive Bayes and Bayesian Networks, Bayesian decision theory
- K Nearest Neighbours
- Linear models: Linear Regression, Logistic Regression
- Support Vector machines, kernel trick
- Ensemble methods: Bias-variance tradeoff, Boosting, Bagging, Random Forests
Unsupervised learning
- Principal Component Analysis
- Clustering: Hierarchical clustering, K-Means
- Expectation Maximisation, Gaussian Mixture Models
Neural networks and deep learning
- Multilayer Perceptrons, Stochastic Gradient Decent, Backpropagation
- Regularisation methods: L1 and L2 regularisation, Dropout, data augmentation
- Convolutional neural networks
- Recurrent Neural Networks, Long Short-Term Memory
- Autoencoders, Embeddings
- Generative Adversarial Networks
- Transfer learning
Practical methodology
- Data preprocessing, Feature extraction and selection
- Performance evaluation, Hyperparameter tuning
Other topics and in machine learning
Reinforcement Learning
Module Skills
| Skills Type | Skills details |
|---|---|
| Application of Number | Inherent to subject |
| Communication | Seminar |
| Improving own Learning and Performance | Inherent to subject |
| Information Technology | Inherent to subject |
| Personal Development and Career planning | Encourages students to see roles in subject for career and personal development |
| Problem solving | Inherent to subject |
| Research skills | Essay |
| Subject Specific Skills | Representation and Reasoning for Intelligent Systems |
Notes
This module is at CQFW Level 7