MRes Computational Statistics and Machine Learning

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PROGRAMME SPECIFICATION
PROGRAMME SPECIFICATION
Programme title:
MRes Computational Statistics and Machine Learning
Final award (BSc, MA etc):
MRes
(where stopping off points exist they should be
detailed here and defined later in the document)
UCAS code:
n/a
(where applicable)
Cohort(s) to which this programme
specification is applicable:
From 2013 intake onwards
(e.g. from 2015 intake onwards)
Awarding institution/body:
University College London
Teaching institution:
University College London
Faculty:
Engineering Sciences
Parent Department:
Computer Science
(the department responsible for the administration of
the programme)
Departmental web page address:
www.cs.ucl.ac.uk
(if applicable)
Method of study:
Full-time
Full-time/Part-time/Other
Criteria for admission to the
programme:
Length of the programme:
The MSc programme is designed for graduates with a first or upper
second-class Honours degree (or equivalent) in a highly quantitative
subject such as computer science, mathematics, electrical
engineering or the physical sciences. Students should also have
some experience with a programming language such as c/c++, Java,
or python. Appropriate industrial experience may also be considered
in some cases.
One year
(please note any periods spent away from UCL, such
as study abroad or placements in industry)
Level on Framework for Higher
Education Qualifications (FHEQ)
(see Guidance notes)
Relevant subject benchmark statement
(SBS)
Masters Level (Level 7)
http://www.qaa.ac.uk/Publications/InformationAndGuidance/Pages/S
BS-Masters-degree-computing.aspx
(see Guidance notes)
Brief outline of the structure of the
programme
and
its
assessment
methods:
(see guidance notes)
Core modules (2x15 credits)
Option modules (3x15 credits)
Dissertation (105 credits)
Assessment: written exams, course work and dissertation.
Board of Examiners:
Name of Board of Examiners:
Board of Examiners for Machine Learning/ Computational Statistics
and Machine Learning
Professional body accreditation
(if applicable):
n/a
Date of next scheduled
accreditation visit:
EDUCATIONAL AIMS OF THE PROGRAMME:
The MRes will teach students the essentials of computational and statistical mathematics vital for an understanding
of large-scale data processing and machine learning. The key aim is to engender students with a deep
understanding of research and in particular to learn to focus in depth on a specific research topic in computational
statistics or machine learning.
At the end of the programme students should be well prepared for entry to a PhD in computational statistics or
machine learning, or for entry to an industrial research programme.
PROGRAMME OUTCOMES:
The programme provides opportunities for students to develop and demonstrate knowledge and understanding,
qualities, skills and other attributes in the following areas:
A: Knowledge and understanding
Knowledge and understanding of:
The fundamental principles of data
analysis and information processing.
The resulting mathematical models and
technologies
Teaching/learning methods and strategies:
Lectures and tutorials
Assessment:
Examination, project and presentation
B: Skills and other attributes
Intellectual (thinking) skills:
Analysis of complex engineering
arguments, analysis of a research
problem
Synthesis of systems integrating basic
components, development of novel ideas
Teaching/learning methods and strategies:
Lectures, tutorials, projects and seminars
Assessment:
Research projects, design exercises
C: Skills and other attributes
Practical skills (able to):
Teaching/learning methods and strategies:
Projects
Design new bespoke mathematical
algorithms to solve problems in largescale data analysis.
Assessment:
Assignment and course projects
Demonstration of ability to construct
solutions to data analysis problems.
D: Skills and other attributes
Transferable skills (able to):
Ability to work in a team; communicate
effectively; project manage; lead
research; use technology appropriately;
use data and literature resources
appropriately
Teaching/learning methods and strategies:
Individual and group projects
Assessment:
Individual and group projects
The following reference points were used in designing the programme:
 the Framework for Higher Education Qualifications:
(http://www.qaa.ac.uk/en/Publications/Documents/Framework-Higher-Education-Qualifications-08.pdf);
 the relevant Subject Benchmark Statements:
(http://www.qaa.ac.uk/assuring-standards-and-quality/the-quality-code/subject-benchmark-statements);
 the programme specifications for UCL degree programmes in relevant subjects (where applicable);
 UCL teaching and learning policies;
 staff research.
Please note: This specification provides a concise summary of the main features of the programme and the
learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes
full advantage of the learning opportunities that are provided. More detailed information on the learning outcomes,
content and teaching, learning and assessment methods of each course unit/module can be found in the
departmental course handbook. The accuracy of the information contained in this document is reviewed annually
by UCL and may be checked by the Quality Assurance Agency.
Programme Organiser(s)
Dr David Barber
Name(s):
Date of Production:
October 2012
Date of Review:
January 2015
Date approved by Head of
Department:
January 2015
Date approved by Chair of
Departmental Teaching
Committee:
Date approved by Faculty
Teaching Committee
January 2015
March 2015
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