Intelligent Systems: Machine Learning & Optimisation - COM00026I
Module summary
Machine Learning & Optimisation.
Module will run
Occurrence | Teaching period |
---|---|
A | Semester 2 2025-26 |
Module aims
This module introduces the field of Artificial Intelligence, key approaches within the field and philosophical questions such as what it means for a machine to understand. Students will learn the theory and practice of machine learning techniques covering linear regression, simple neural networks, linear algebra and continuous optimisation. 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 Python, and the group project will introduce the students to a Python-based modern machine learning library such as TensorFlow or PyTorch.
Module learning outcomes
-
Explain the difference between strong, weak and general AI, understand the relationship between computation and AI, define the machine learning paradigm, and distinguish it from the wider field of AI
-
Compute partial derivatives and understand the concept of the gradient as a generalisation of the derivative
-
Express, manipulate and solve systems of linear equations using linear algebra, and apply linear regression and logistic regression
-
Optimise multivariate functions using gradient descent
-
Explain the concept of overfitting and how regularisation can be used to prevent it
-
Construct a basic neural network using a modern machine learning library and learn its weights via optimisation using the backpropagation algorithm
-
Deconstruct ethical arguments relating to AI and its applications, and appreciate the ethical and privacy implications of machine learning
Indicative assessment
Task | % of module mark |
---|---|
Essay/coursework | 30 |
Online Exam -less than 24hrs (Centrally scheduled) | 70 |
Special assessment rules
None
Indicative reassessment
Task | % of module mark |
---|---|
Essay/coursework | 30 |
Online Exam -less than 24hrs (Centrally scheduled) | 70 |
Module feedback
Feedback is provided through work in practical sessions, and after the assessments as per normal University guidelines.
Indicative reading
-
Artificial Intelligence: A Modern Approach by Russell and Norvig