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).

Academic session

2018-19

Module

Machine Learning for Domain Specialists

Code

COMP0142

Module delivery

1819/A6U/T2/COMP0142 Undergraduate

Related deliveries

1819/A7P/T2/COMP0142 Postgraduate

Prior deliveries

None - new delivery for 1819

Level

Undergraduate

FHEQ Level

L6

FHEQ credits

15

Term/s

Term 2

Module leader

Hosseini, Dariush

Contributors

Hosseini, Dariush

Module administrator

Ball, Louisa

Aims

Introduce the students into machine learning basics applied to domain specialists.

Learning outcomes

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:

  • BSc Computer Science (Year 3)
  • MEng Computer Science (Year 3)
  • MEng Mathematical Computation (Year 3)
  • BASc Arts and Sciences (Sciences and Engineering)
  • BSc/MSci Information Management for Business /3
  • BSc/MSci Natural Sciences
  • BSc Security and Crime Science
  • iBSc Mathematics, Computers and Medicine

Prerequisites:

Students should have ideally competency in the following subjects:

  • basic probability; and
  • calculus; and
  • programming

Content

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.

Delivery

The module is delivered through a combination of lectures, tutorials, written and programming exercises, and two one-hour in-class tests.

Assessment

This module delivery is assessed as below:

#

Title

Weight (%)

Notes

1

In-class test 1

30

One hour written test, mid-term

2

In-class test 2

30

One hour written test, end of term

3

Coursework 1

10

Due end of week 3 (Jan 25, 2019)

4

Coursework 2

10

Due end of week 6 (Feb 15, 2019)

5

Coursework 3

10

Due end of week 8 (March 1, 2019)

6

Coursework 4

10

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 40.00%;

AND, when taken as part of BSc Computer Science; MEng Computer Science, and MEng Mathematical Computation:

  • achieve a mark of at least 30.00% in any Components of assessment weighed ≥ 30% of the module.

Where a Component comprises multiple Assessment Tasks, the minimum mark applies to the overall component.