COMP0088 Introduction to Machine Learning
This database contains the 2018-19 versions of syllabuses.
Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).
Introduction to Machine Learning
To have a full understanding of the learning outcomes.
On successful completion of the module, a student will be able to:
- understand machine learning at both the theoretical and practical level.
- solve real-world machine learning problems using the right tools.
Availability and prerequisites
This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.
In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.
Programmes on which available:
There are no formal prerequisites. It is however highly recommended that students who select this module have competency in linear algebra, calculus, probability theory, and programming.
Introduction to Supervised Learning
- Linear models for regression and classification: least squares, logistic regression
- Concepts of overfitting and regularization, L1 and L2 regularisation
- Boosting, Decision Trees, Random Forests
- Support Vector Machines
- Deep Learning: Neural Networks for regression and classification, Recurrent Neural Networks
Introduction to Unsupervised Learning
- K-means, Principal Components Analysis, Sparse Coding
- Expectation-Maximization, Mixture of Gaussians, Factor Analysis
- Deep Autoencoders, Generative Adversarial Networks
An indicative reading list is available via http://readinglists.ucl.ac.uk/departments/comps_eng.html.
The module is delivered through a combination of lectures and programming exercises.
This module delivery is assessed as below:
Written examination (2hrs 30mins)
In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.