COMP0078 Supervised 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).

Academic session

2018-19

Module

Supervised Learning

Code

COMP0078

Module delivery

1819/A7U/T1/COMP0078 Masters (MEng)

Related deliveries

1819/A7P/T1/COMP0078 Postgraduate

Prior deliveries

COMPM055

Level

Masters (MEng)

FHEQ Level

L7

FHEQ credits

15

Term/s

Term 1

Module leader

Herbster, Mark

Contributors

Herbster, Mark

Module administrator

Ball, Louisa

Aims

This module covers supervised approaches to machine learning.

Learning outcomes

On successful completion of the module, a student will be able to:

  1. gain in-depth familiarity with various classical and contemporary supervised learning algorithms.
  2. understand the underlying limitations and principles that govern learning algorithms and ways of assessing and improving their performance.

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:

  • MEng Computer Science (International Programme) (Year 4)

  • MEng Computer Science (Year 4)

Prerequisites:

To be eligible to select this module, students must have:

  • strong competency in basic mathematics, calculus, probability, and linear algebra

Content

The module consists of both foundational topics for supervised learning such as Linear Regression, Nearest Neighbors and Kernelisation as well contemporary research areas such as multi-task learning and optimisation via proximal methods. In a given year topics will be drawn non-exclusively from the following:

  • Nearest Neighbors
  • Linear Regression
  • Kernels and Regularisation
  • Support Vector Machines
  • Gaussian Processes
  • Decision Trees
  • Ensemble Learning
  • Sparsity Methods
  • Multi-task Learning
  • Proximal Methods
  • Semi-supervised Learning
  • Neural Networks
  • Matrix Factorization
  • Online Learning
  • Statistical Learning Theory

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 and problem classes.

Assessment

This module delivery is assessed as below:

#

Title

Weight (%)

Notes

1

Written examination (2hrs 30mins)

75

 

2

Coursework

25

 

In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.