COMP0142 Machine Learning for Domain Specialists
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).
Machine Learning for Domain Specialists
None - new delivery for 1819
Introduce the students into machine learning basics applied to domain specialists.
On successful completion of the module, a student will be able to understand basics of data analytics, recognize which methods should be applicable to analyze which types of data, and perform data analysis tasks.
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:
Students should have ideally competency in the following subjects:
The module introduces basics of machine learning (ML) methodology applied to analyze real-world data in biomedical applications. General theory and applications are presented in classes, with specific problems being worked out in practicals.
The module includes:
- ML tasks (description, prediction, dimensionality reduction, data integration),
- types of learning (unsupervised, supervised, reinforcement),
- mathematical foundations (linear algebra, calculus, probability, statistics),
- unsupervised learning concepts and algorithms (k-means, hierarchical)
- supervised learning: regression (linear, multiple, non-linear) and classification (kNN, decision trees, logistic regression, SVM, naïve Bayes classifier)
- Bayesian networks (BN)
- Neural networks and Deep learning
- Reinforcement learning basics
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, tutorials, written and programming exercises, and two one-hour in-class tests.
This module delivery is assessed as below:
In-class test 1
One hour written test, mid-term
In-class test 2
One hour written test, end of term
Due end of week 3 (Jan 25, 2019)
Due end of week 6 (Feb 15, 2019)
Due end of week 8 (March 1, 2019)
Due end of week 11 (March 22, 2019)
In order to pass this module delivery, students must:
- achieve an overall weighted module mark of at least 40%; and
- achieve a mark of at least 30% in any components of assessment weighed ≥ 30% of the module.
Where a component comprises multiple assessments, the minimum mark applies to the overall component.