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Transcript
Programme Title: Advanced Computing – Machine Learning, Data Mining & High Performance Computing MSc
This document shows which mandatory units contribute towards the programme’s intended learning outcomes. By looking at the relevant units it is therefore possible to
establish how programme intended learning outcomes are assessed. For programmes with limited or no mandatory units the units shown below will be an indication of the
most frequently chosen optional units and how this choice meets the programme’s intended learning outcomes.
Taught and assessed through
the following mandatory*
units:
Programme intended
learning outcomes
mapped by unit
COMS30301
EMATM1120
EMATM0012
COMS30004
COMS30106
COMSM0305
COMS30003
EMATM0004
A2, A3, A4, A11
A4, A6, A7, A8, A10, A12
A4, A11
A1, A3, A4, A5,
A6, A7, A8, A10, A12
A4, A6, A8, A9
A3, A4
A3, A4
A: Knowledge and Understanding of:
1.
2.
3.
The fundamental high performance computing techniques;
The fundamental machine learning techniques;
The scope and limitations of current approaches to High Performance Computing, Machine Learning and
Data Mining;
4. Ways to improve existing techniques or to develop new techniques and algorithms;
5. The field of Parallel Programming and problem decomposition;
6. The field of Artificial Intelligence (AI) and intelligent reasoning;
7. The contribution of AI to the solution of difficult problems;
8. The main techniques for intelligent automated reasoning, and for knowledge representation in logic;
9. The contribution of evolutionary computing to machine intelligence;
10. The main techniques for intelligent automated reasoning, and for knowledge representation in logic;
11. The theoretical limitations of current approaches to machine learning;
12. The contribution and limitations of the added expressiveness of logical representations.
B: Intellectual Skills /Attributes:
1.
2.
3.
4.
5.
6.
7.
8.
Determine and justify, given a problem, the suitability of High Performance Computing, Machine
Learning and Data Mining techniques to the solution of that problem;
Select, given a specific application, the most appropriate combination of high performance computing,
learning and mining techniques to use;
Design and implement High Performance Computing, Machine Learning and Data Mining solutions to
suitable problems;
Use skills and understanding necessary to appreciate and use AI techniques for the solution of
challenging problems;
Recognise the types of problems where AI techniques may be used;
Use logic and other forms of knowledge representation to represent suitable tasks;
Apply symbolic machine learning algorithms to structured data;
Apply high performance computing and data mining techniques to large, real-world datasets.
COMS30301
EMATM1120
EMATM0012
COMS30004
COMS30106
COMSM0305
COMS30003
EMATM0004
B1, B2, B3, B6
B4, B5, B6,
B1, B2, B3, B6, B7
B1, B2, B3, B8
B4, B5, B6,
B3, B6
B1, B2, B3, B8
B1, B2, B3, B8
COMS30301
COMS30106
COMS30004
COMSM2202
COMSM3100
C3, C4
C3
C2
C1, C4
C1, C4
C: Other Skills /Attributes (Practical/Professional/Transferable):
1.
2.
3.
4.
5.
Communicate ideas and concepts clearly, both orally and in writing;
Apply HPC techniques by means of parallel programming languages and using the universitys BlueCrystal
supercomputer;
Apply ML techniques by means of declarative programming languages;
Use machine learning systems to solve practical problems;
Undertake a large-scale research project.
*Or indication of the most frequently chosen optional units.