MSc Degree Guide 2008/2009


(last updated 16th October 2008)

Contents:

Degree Overview
Help and Advice
Specialisms by Degree
Choosing Courses
Compulsory Courses
Courses for specific Specialisms:
    Analytical and Scientific Databases
    Bioinformatics, Systems and Synthetic Biology
    Computer Systems and Software Engineering
    Fundamentals of High Performance Computing Systems
    Geoinformatics
    Intelligent Robotics
    Knowledge Representation and Reasoning
    Learning from Data
    Music Informatics
    Natural Language and Language Engineering
    Neural Computation and Neuroinformatics
    Theoretical Computer Science
Programming Requirement
Courses of General Interest
Coursework and Examinations
Diploma requirements
Plagiarism

Please review this material on plagiarism first. It is essential you understand and adhere to our policies. The University has issued general guidance on plagiarism.


Degree Overview

This Document forms part of the official guidelines for the following degrees:

The degrees are each split into two halves: two semesters of lecture-based teaching, followed by a major individual project in a chosen area of specialisation. Together, these span a full twelve month period. The lectures are followed by examinations in December and April/May. Thereafter, students pursue their project work and write a dissertation on the project which is assessed in August/September. Details of arrangements for projects will appear on the project pages.
It is possible to request a change of degree. First ensure that your change is allowed by your funders and that your course and specialism choices are allowed under the next degree. Then submit an ITO Contact request stating the change and confirming that this is allowed by your funding agency (if appropriate). Note that college do not allow multiple changes so only submit a request if you are very sure.


Help and Advice

For help in choosing courses or academic advice contact the Specialism advisors

For help any any administrative issue contact the ITO which is best done through the support contact form

If you run into any difficulties during your course here (e.g. health, personal, financial) then you should contact your MSc Directors of Studies (Michael Rovatsos or Bob Fisher see http://www.inf.ed.ac.uk/teaching/years/msc/ for details of which specialism is assigned to which DoS). This is extremely important. They will look to see what support we can offer and importantly make sure any appropriate measures are put in place at the Boards of Examiners (this is done in confidence). If we are not made aware of issues before the Board of Examiners and ideally as they happen we are not allowed to consider them until after an appeal which will result in lengthy delays to your study. The Directors of Studies maintain a high level of confidentiality and so any problems you discuss will not be aired openly, your DoS will report that there is a 'problem' and its severity, not discuss the details in any of the boards.

Feedback and Staff / Student structures

You will be asked to fill in a lot of feedback forms for the School, College, University and external agencies. We take the feedback on these forms seriously so we would appreciate it if you could take the time to complete as many of them as possible.

In addition to the feedback forms there is a regular staff-student meeting hosted by the Director of Teaching with the year representatives. Year reps are recruited at the start of the year, look out for emails on this if you want to get involved.

If you have an issue with a specific course, the normal action would be to raise this directly with the course lecturer. We are open to suggestions and comments. If you feel unable to contact the lecturer directly, talk to one of the year reps or if required the Course Organiser.


The Specialisms

Each student follows a major and minor specialism within their Degree. The specialisms are: There are some restrictions on which specialisms you may take. If you are funded under the EPSRC Collaborative Training Account or if you are funded under the Stanford Link to study Language Engineering, then you are locked into a major specialism choice. Other funding agencies may have other resrtictions, please check. Otherwise, the restrictions on which specialism you can take as a major, depend on your Degree:
M.Sc in Artificial Intelligence Intelligent Robotics, Knowledge Representation and Reasoning, Learning from Data, Natural Language and Language Engineering.
M.Sc in Cognitive Science Natural Language and Language Engineering, Neural Computation and Neuroinformatics.
M.Sc. in Computer Science Analytical and Scientific Databases, Computer Systems and Software Engineering, Fundamentals of High Performance Computing Systems, Theoretical Computer Science.
M.Sc in Informatics No restrictions apply, you may choose any specialism as major.

For all degrees, there is no restriction on the minor you choose. The following specialisms can be taken as both major and minor: Bioinformatics, Systems and Synthetic Biology, Fundamentals of High Performance Computing Systems, Neural Computation and Neuroinformatics, Learning from Data, Computer Systems and Software Engineering.

Note this breakdown means that if you want to major in Bioinformatics, Systems and Synthetic Biology, Music Informatics or Geoinformatics then you MUST be registered for the MSc in Informatics.


Choosing Courses

Please complete the Course Registration Form by Sep 26. You do not need to see your "Director of Studies" to choose courses. Please see your specialism advisor if you want advice.

On the form you will have to choose 60 points worth of courses per semester. Several selections will have been made for you already. All MSc students must take the compulsory courses IRR and IRP. In addition all students should check the programming requirement to check if they need to take IJP (Java) or LP (Prolog). Beyond these rules, course choice will follow your specialisms and interests:

You must take at least three courses from your major specialism and at least two courses from your minor specialism, which should be core courses (in bold) where possible. If you are taking one of the specialisms as both major and minor, then you must take at least five courses from the expanded list (see below), again core courses where possible.

In addition to the courses of direct interest for particular specialisms, there is a set of programming courses and a number of courses of general interest.

Each course is assigned a Level. M.Sc. courses are all level 11. In the timetable you will be able to choose from a restricted list of level 9 and level 10 courses (these are in italics). These are specifically designed for third year and fourth year undergraduates but would be of interest and value as part of an M.Sc. degree. You are limited to a maximum of 30 points worth of these courses. If you want to take a level 9 course that is not listed anywhere in this document you should request permission from the Course Organiser. You do not have to take any.

Each lecture course normally consists of up to twenty one-hour lectures (two per week for one semester) together with associated coursework and background reading. A few courses may also have tutorials, labs or a different structure. All courses have associated assignments. Part of this is assessed during the course - you will be given assignments to complete by deadlines set by the course lecturer. The relative weightings of the assignments and examination in the final mark for each course are given in the detailed course descriptions. Remember also that a substantial part of the assessment for most courses is by an exam which may ask questions related to any aspect of that course.

Each Lecture course will be worth either 10 or 20 points (nominally equivalent to 100 or 200 hours' student effort). Most courses in Informatics are 10 points but some 20 point courses are available. You can take a maximum of two 20 point courses, again you do not have to take any.

You should select 60 points' worth of courses per semester including the compulsory courses.

Deadlines and changes
You may change course choices after the initial selection but there are deadlines. These are:
Semester 1 courses: initial selection by end of week 1, final choices (i.e. no changes) by wednesday of week 4.
Semester 2 courses: initial selection by week 1, semester 1, final choices by friday of week 2, semester 2.

Please note, this final deadline in semester 2 cannot be moved. All course choices after that date are final.

In summary, you should:

For further advice, please contact your specialism advisor


Compulsory Courses

All M.Sc. students must take the Informatics Research Review course in Semester 1 and Informatics Research Proposal course in Semester 2. These courses have no lectures and no exams and follow a different format. They are designed to introduce you to research activity in Informatics in your specialism area from the start of the degree. Informatics Research Review gives you an opportunity to survey literature on a particular topic within your degree specialism. Informatics Research Proposal allows you to build towards your summer research project. These courses have a simple PASS/FAIL grade only.

See also the programming requirement section as you may be required to take a course in programming skills.


Courses for Specific Specialisms

The constraints on your choice of course for each MSc area is shown below. You can find links to detailed course descriptions on the main MSc page. Core courses (in bold typeface) are ones which we suggest you take in preference to any of the other courses listed for that specialism.  Recommended courses (non-bold typeface) are ones which are particularly compatible with a specialism but are not considered essential to that specialism . You must register for 60 points of courses in Semester 1 and 60 points of courses in Semester 2. These must include 3 courses for your major specialism and 2 courses for your minor specialism, with the remainder coming from the pool of available courses (shown on the main MSc page). Remember these compulsory courses count as 40 points of your total 120 points. You should make sure that you do not choose courses which clash in the lecture timetables.  Please note that we always end up with some clashes between courses. We can do very little to avoid these but please let us know of any so we can try to avoid them in future years.

Analytical and Scientific Databases

This specialism brings together topics in advanced database design, theory and implementation that will be applicable to the applied as well as the research fields. There are two central academic outcomes to this programme. The first is to bring the students up to speed with the latest technology in Database Science and in the analysis of complex databases. The second aim is to introduce the students to the research active areas in the field within the context of a range of real example programmes.
 
Semester 1
Semester 2
Major
OR
Minor
Applied Databases AD
Data Integration DIE
Bioinformatics 1 BIO1
Introductory Applied Machine Learning IAML
Probabilistic Modelling and Reasoning PMR
Advanced Databases ADBS
Querying and Storing XML QSX
Topics in Distributed Databases TDD
Bioinformatics 2 BIO2

Bioinformatics

The aim of the bioinformatics, systems and synthetic biology specialism is to familiarise students with biological data, their storage and analysis, how they integrate at a systems level, how this is studied, modelled and the emerging field of synthetic biology where biological components and systems can be constructed from first principles. In particular, students should understand what information can be extracted from biological data (e.g., information related to phylogenetic trees, biological networks, protein structure and function, developmental processes, genetic correlates of disease, etc.) and what techniques can be used for extracting and modelling such information. Students who complete the course will be prepared for employment in the bioinformatic sector of pharmaceutical and biotech industries or for entry into a PhD programme.
 
Semester 1
Semester 2
Major
OR
Minor
OR
Combined
Bioinformatics 1 BIO1
Synthetic Biology: Modelling SBM
Information Processing in Biological Cells IPBC
Modelling and Simulation MS
Applied Databases AD
Introductory Applied Machine Learning IAML
Probabilistic Modelling and Reasoning PMR
Bioinformatics 2 BIO2
Computational Systems Biology CSB
Models and Languages for Computational Systems Biology MLCSB
Reinforcement Learning RL
Machine Learning and Pattern Recognition MLPR
Data Mining & Exploration DME
Neural Computation NC

Computer Systems and Software Engineering

This specialism embraces both the theory and the practice of designing programmable systems, with topics ranging from advanced programming concepts to the design of computer systems and software engineering. As with other specialisms, this specialism prepares students for Ph.D. study and for careers in the software industry.
 
Semester 1
Semester 2
Major
OR
Minor 

Combined
Major/Minor

Computer Graphics CG
Computer Networking CN
Design and Analysis of Parallel Algorithms DAPA
Distributed Systems DS
Human Computer Interaction HCI
Modelling & Simulation MS
Software Engineering with Objects & Components SEOC
Advanced Databases ADBS
Compiler Optimisation COPT
Parallel Architectures PA
Parallel Programming Languages and Systems PPLS
Software Architecture, Process and Management SAPM
Software Testing ST
Research Topics in Software Engineering RTSE

Fundamentals of High Performance Computing Systems

Fundamentals of High Performance Computing (part of the Computer Systems and Software Engineering specialism) aims to equip students with the intellectual tools, knowledge and practical skills they will need to become developers and exploiters of the parallel and distributed systems which increasingly underpin high performance computing activities. The focus is on understanding fundamental design principles and choices and could lead to PhD study in the area, or to employment with corresponding technology developers.
Modules marked "EPCC" are drawn from the MSc in High Performance Computing offered by the Edinburgh Parallel Computing Centre. These have an application oriented focus which neatly complements the stance taken in the FHPCS specialism. EPCC courses are open only to students who take the FHPCS theme as their major specialism . Places for Informatics students on EPCC courses are limited to at most six students per course, and eligible students may take no more than two such courses. Students wishing to do so must contact the specialism advisor by e-mail (bfranke@inf.ed.ac.uk), by 5pm on Thursday 20th September, indicating the modules of interest in order of preference (any number, but remember that at most two will be allocated to you). Places will then be allocated, with preference given to students who are taking FHPCS as both their major and minor specialism. Requests received after the deadline will be considered if places remain available.
 
 
Semester 1
Semester 2
Major
OR
Minor
OR
Combined
Design and Analysis of Parallel Algorithms DAPA
Distributed Systems DS
Message Passing Programming [EPCC] -
Shared Memory Programming [EPCC] -
Parallel Decomposition [EPCC] -
Parallel Architectures PA
Parallel Programming Languages and Systems PPLS
Advanced Databases ADBS
Compiler Optimisation COPT
Data Mining and Exploration DME
Software Testing ST
Exploiting The Computational Grid [EPCC] -
Applied Numerical Algorithms [EPCC] -

The EPCC courses are timetabled as one timetabled block per week (including practical sessions), click here for further information on these courses.

Geoinformatics

This specialism brings together subject matter from Informatics and Geosciences, and includes a number of strands.  Climate and environmental change pose huge challenges to our understanding of natural systems.  Informatics methods in data organisation and analysis, and modelling and simulation play an increasing role in addressing these challenges.  At a societal level, geographic information is increasingly pervasive in information processing (e.g. Oracle Spatial or mashups with Google Maps), and its ubiquitous nature poses new research challenges and provides new opportunities for novel and interesting applications. The aim of the Geoinformatics specialism is to introduce students to the unique nature of geoscience information and associated algorithms, methodologies and software. The specialism is designed to benefit from the growing synergies between Informatics and GeoSciences, and to support students in mastering an emerging and exciting area of research. Students who complete the specialism will be well equipped to carry out further research or to pursue a career in a range of industries that deal with geoscientific information.
 
Semester 1
Semester 2
Major
OR
Minor 
Principles of GIS (GEO) -
Introductory Applied Machine Learning IAML
Fundamentals of AI FAI
Human Computer Interaction HCI
Applied Databases AD
Spatial Modelling (GEO) -
Computational Methods for Global Change Research CMGCR
Geovisualisation (GEO) -
Knowledge Modeling and Management KMM
Data Mining and Exploration DME
Knowledge Representation and Inference KRE
Advanced Spatial Databases (GEO) -

This specialism is joint with Geosciences. Please also see the MSc GIS website for further information on the programme and (GEO) courses.

Intelligent Robotics

The aim of the Intelligent Robotics specialism is to prepare students for entry into Ph.D. programmes or for employment as research workers in Intelligent Robotics and related areas in higher education or industrial/commercial research laboratories undertaking research and development in robotics and intelligent control applications.
 
Semester 1
Semester 2
Major
OR
Minor 
Introduction to Vision and Robotics IVR
Introductory Applied Machine Learning IAML
Probabilistic Modelling & Reasoning PMR
Automated Planning PLAN
Genetic Algorithms & Genetic Programming GAGP
Advanced Vision AV
Intelligent Autonomous Robotics IAR
Reinforcement Learning RL
Structure and Synthesis of Robot Motion SSRM
Computer Animation RL

 

Knowledge Representation and Reasoning

The aim of the Knowledge Representation and Reasoning specialism is to prepare students for entry into Ph.D. programs or employment in academic research, industrial and commercial research and development groups, or in product development and marketing groups concerned with building and supplying knowledge-based systems and other knowledge-based applications.
 
Semester 1
Semester 2
Major
OR
Minor
Automated Reasoning AR
Logic Programming LP
Fundamentals of AI FAI
Human Computer Interaction HCI
Introductory Applied Machine Learning IAML
Automated Planning PLAN
Applied Databases AD

Knowledge Modelling and Management KMM
Multi-agent Semantic Web Systems MASWS
Cognitive Modelling CM
Knowledge Representation and Inference KRI
Software Architecture, Process and Management SAPM

Learning from Data

Increasing amounts of data are being captured, stored and made available electronically. The aim of the Learning from Data specialism is to train students in techniques to analyze, interpret and exploit such data, and to understand when particular methods are suitable and/or applicable. These techniques derive from disciplines such as machine learning, probabilistic and statistical modelling, pattern recognition and neural networks, and are sometimes collectively referred to as data mining. The specialism will prepare students for entry into PhD programmes or for employment in commercial environments and/or scientific/engineering research.
 
Semester 1
Semester 2
Minor
Introductory Applied Machine Learning IAML
Probabilistic Modelling & Reasoning PMR
Genetic Algorithms & Genetic Programming GAGP
Machine Learning and Pattern Recognition MLPR
Reinforcement Learning RL
Major
Introductory Applied Machine Learning IAML
Probabilistic Modelling & Reasoning PMR
Genetic Algorithms & Genetic Programming GAGP
Machine Learning and Pattern Recognition MLPR
Reinforcement Learning RL
Data Mining & Exploration DME
Combined
Major/Minor 
Introductory Applied Machine Learning IAML
Probabilistic Modelling & Reasoning PMR
Applied Databases AD
Genetic Algorithms & Genetic Programming GAGP
Text Technologies TT
Machine Learning and Pattern Recognition MLPR
Data Mining & Exploration DME
Reinforcement Learning RL
Visualisation VIS
Structure and Synthesis of Robot Motion SSRM
Neural Information Processing NIP

Note: For a Minor in Learning from Data, a student may choose to substitute Machine Learning and Pattern Recognition for the required course Introductory Applied Machine Learning (IAML).


Music Informatics

I will tidy this up later but the information is good to start planning with:

The Music Informatics specialism gives an opportunity to study the structure, behaviour and interactions of natural and engineered systems engaged in musical activity. This can be done from the view of physical modelling of musical sounds and insstruments; machines analysis of music, in real time or otherwise; using computers in many ways in the production of music and sound in general; and in studying musical interaction between (natural or artificial) performers.

For the combined major/minor, students should take, in addition to IRR and IRP, at least 30 points of courses from Informatics/PPLS, and 30 points from the school of Arts, Culture and the Environment (ACE). There may be restrictions on numbers taking some ACE courses. Courses in ACE count for 20 credit points.

Semester 1 Semester 2
Sound Design Media [ACE]
Sonic Structures [ACE]
Real-Time performance strategies [ACE]
Musical Applications of Fourier Analysis [ACE]
Introductory Applied Machine Learning
Probabilistic Modelling and Reasoning
Computer Graphics
Fundamentals of AI
HCI
Introduction to Cognitive Science
Speech Processing [PPLS]
Interactive Sound Environments [ACE]
Digital Media Studio Project [ACE]
Non Real-Time Systems [ACE]
Electro-acoustics Composition [ACE]
Automatic Speech Recognition
Multi-agent Semantic Web Systems
Machine Learning and Pattern Recognition
 

Natural Language and Language Engineering

The aim of the Natural Language and Language Engineering specialism is to prepare students for entry into Ph.D. programmes or for employment as research workers in AI and related areas in higher education or industrial/ commercial research laboratories undertaking research and development in natural language processing. The specialism requirements are different depending on whether the focus is Natural Language or Language Engineering. The Natural Language requirements are:
 
Semester 1
Semester 2
Major
OR
Minor
Advanced Natural Language Processing (ANLP)

Text Technologies
TTS
Sentence Comprehension [PPLS] -
Statistical and Experimental Design [PPLS] -
Language Production [PPLS] -
Introduction to Phonology and Phonetics [PPLS] -
Automated Speech Recognition ASR
Machine Translation MT
Visual Word Recognition [PPLS] -
Advanced topics in phonology: Prosody [PPLS] -
The Language Engineering (combined major and minor) requirements are:
 
Semester 1
Semester 2
Combined
Major/Minor
Advanced Natural Language Processing (ANLP)
Speech Processing [PPLS] -
Introductory Applied Machine Learning IAML
Text Technologies TTS
Statistical and Experimental Design [PPLS] -
Computer Programing for Speech and Language Processing[PPLS] -
Machine Translation MT
Machine Learning and Pattern Recognition MLPR
1 of (Speech Synthesis [PPLS] or
-
Automated Speech Recognition) ASR
Visual Word Recognition [PPLS] -

 

Neural Computation and Neuroinformatics

This specialism prepares students for entry into Ph.D. programmes or for employment as research workers at the intersection of the study of the brain and the study of its computation. It ranges from the study of cellular and subcellular computational processes through behavioural processes, to software methodologies for brain research - the emerging field of  neuroinformatics.  In particular, students will be well prepared by this specialism to apply for entry to the Doctoral Training Centre in Neuroinformatics in Edinburgh.
 
 
Semester 1
Semester 2
Major/Minor or Combined
Bioinformatics 2 Bio2
Probabilistic Modelling and Reasonibg PMR
Applied Databases AD
Informatics Research Methodologies IRM
Statistical and Experimental Design [PPLS] -
Neural Computation NC
Computational Cognitive Neuroscience CCN
Bioinformatics 2 BIO2
Cognitive Modelling
CM
Computational Neuroscience of Vision CNV
Neural Information Processing NIP

 

Theoretical Computer Science

The primary aims of the theoretical courses are to introduce students to core areas of theoretical Computer Science, to provide practical experience of that theory and to introduce students to the technologies through which theory-based tools are implemented, including preparation for Ph.D. study. The courses offered combine a good grounding in the core areas of the subject with experience in the practical application of theory across a range of theory-based Software Engineering tools. These courses will be of particular interest to students with a mathematics background. The practical components of these courses will consider both the use and implementation of tools. Each of the courses in this specialism aims to provide a balance between theoretical topics and their application in software development. In many of the courses the theory suggests the construction of tools to aid software production. Students will meet a variety of these tools during the course and will have the opportunity to develop skills in their use as well as studying the techniques used in their implementation.
 
Semester 1
Semester 2
Major
OR
Minor
Automated Reasoning AR
Data Integration and Exchange DIE
Modelling & Simulation MS
Communication and Concurrency COC
Algorithmic Game Theory and its Applications AGTA
Models and Languages for Computational Systems Biology MLCSB
Research Topics in Software Engineering RTSE
Topics in Distributed Databases TDD
Advances in Programming Languages APL
Computer Algebra CA
Computational Complexity CMC
Language Semantics and Implementation LSI
Types and Semantics for Programming Languages TSPL
Querying and Storing XML QSX

Programming Requirement

All MSc students should be able to program by the time they leave the School of Informatics. MSc in AI students should also be able to program in Prolog. Students registered for the MSc in Cognitive Science and Natural Language may substitute Prolog for Java if they wish (i.e., either Prolog OR Java is sufficient).

The programming courses you will need to take depend on your capabilities at the time you enter the course. There are two programming courses, all in Semester 1: Introduction to Java Programmes covers JAVA and Logic Programing covers Prolog. Your choice of programming course(s) will depend on your prior experience, the requirement describe above (but see exemptions below), the other courses you wish to take (e.g. Prolog is required for some other courses, particularly in the areas of language, cognitive modelling and reasoning), and the type of project you expect to do in the second half of the course (some will require a specific language).

Exemptions: Students who already satisfy the requirement (at least to the extent that they would have no problem doing their MSc project in the relevant language) may be excused from taking one or more programming courses. Some students may enter the program already familiar with what we will consider as Java-equivalent (other object-oriented languages such as C++) or Prolog-equivalent (other AI-specific languages such as Lisp) and these can also be grounds for exemption from one or both language requirements. Students claiming exemption should produce documentary evidence of coursework or programming experience, or be prepared to affirm in writing that they have the relevant experience, show it to their specialism advisor who grants the exemption, and ensure that the ITO has a copy of this evidence or affirmation.

If you wish to claim exemption from the programming requirement, you should obtain the approval by email from your specialism advisor. In your email requesting exemption you should describe your past experience which you believe qualifies you for an exemption. The faculty member may ask you to go for a talk in person in some cases. You should also provide copies as indicated above to the ITO.


Courses of Interest to Everyone

The course Fundamentals of AI (FAI, Semester 1), Cognitive Modelling (CM, Semester 2) and Text Technologies (TTS, Semester 1) will be of interest to everyone but especially those students wanting to get their MSc degree in Artificial Intelligence or Cognitive Science.

The School also runs two courses on Informatics Entrepreneurship (IE1 and IE2) that are open to all MSc students.

You may also be interested in courses taught by another School, a sample of these courses appears here.


Flexibility

The specialism structure outlined above, including required courses, is expected to be adhered to by almost all students. If you wish to take a set of courses which do not conform to this structure you will need to present good reasons why this makes sense in the context of the degree you are registered for and the background you already have.

Coursework and Examinations

The University Postgraduate (taught) examination regulations apply to all the MSc degrees covered in this course guide.

The MSc is examined on its taught component comprising coursework and examination and on the dissertation which you start immediately after the May exams. You must pass both the taught part and dissertation to pass on the MSc overall. If, for example, you achieve only Diploma level on the 120 point taught component of the course then you will not be allowed to undertake a project.
Coursework is delivered during the semester and where a course has 30% or less of the marks you should receive feedback and marks at the time. If the course has more than 30% of the marks from coursework, it will be treated as a term paper and not returned. All marks returned during the semesters should be treated as provisional until after the Board of Examiners meets after the main examination period.

How to pass at MSc level: To continue onto the MSc project you must meet severa criteria:

  1. An average grade of 50% over the 80 points of graded courses (i.e. not including IRR or IRP)
  2. A pass in at least 80 points of courses (i.e. all courses including IRR and IRP).
Late submission policy.

Normally, you will not be allowed to submit coursework late.

If you have a good reason to need to submit late, you must do the following.
1. Read this section carefully, especially the "good reasons" for late submissions.
2. Request an extension (i.e. a specific length of time in days) and identify the affected course.
3. Submit the request via the ITO Contact Form

"Good reason" for an extension means something that, in the judgement of the member of staff responsible, would prevent a competent, well-organised, conscientious student from being able to submit on time. Examples include:

You should always inform your Director of Studies of any such thing that seriously affects your work, whether or not you ask for an extension as a consequence. If you prefer, you can choose to discuss details *only* with your DoS; s/he can advocate with other members of staff for you without going into details.
Non-examples, things that would not be considered good reasons, include anything you could have planned for or avoided: difficult clusters of deadlines, attending social events, the demands of any job you undertake during semester, last-minute computer problems, loss of work through (your) backup failure, etc.

In general, you are expected to plan your time well and including contingency time. For example, if you expect a piece of work to take two days, you should begin it more than two days before its deadline.

Note that this policy varies in other schools and external courses may have different late submission rules.

Exams

Most examinations for the MSc courses in Informatics take place at the end of Semester 2. A small number of courses may examine in semester 1 and this may also be true of external courses.
The correspondence between numerical scores, grades and their interpretation in terms of the MSc is given below.
 
 

Score  Grade  Interpretation 
>= 70  Excellent 
60-69  Very Good 
50-59  Good 
40-49  Satisfactory for Diploma but inadequate for MSc 
< 40  Unsatisfactory 

Written examinations take place in December and then during the first weeks of the summer. They can spread over three weeks or more so be careful to check when your exams take place before arranging any absences from Edinburgh. There is one examination paper per course and each paper typically lasts 2 hours. Each paper normally is set by the lecturer responsible for each course and is vetted by appropriate members of the Board of Examiners. Questions may be set on any aspect of the lectures or coursework.

The Board of Examiners comprises the External examiner, the Head of School, the Course Organiser and the lecturers on the course. Your overall coursework mark is decided at a Board of Examiners meeting, usually held in May. The Board has the freedom to aggregate marks in any way but normally each paper is given equal weighting. The Board may take mitigating circumstances (e.g. illness) into account so it is vital that you communicate these to the MSc administrative staff, along with substantiating evidence (e.g. a medical certificate), if you believe that your performance has been impaired significantly. If you are ill on or around the date of an examination then you must obtain a medical certificate from a doctor as soon as you are fit enough to do so. Your project mark is decided at a second Board of Examiners meeting in October, along with your overall mark for the course. The Board of Examiners can award distinctions to students who have performed exceptionally well on both coursework (close to or above 70) and project work (at least 70).


Appeals

You have a right of appeal against a decision of the Board of Examiners. This must be made within six weeks of the release of results. The only grounds for an appeal are irregularities in the conduct of the assessment or the Board of Examiners not having all available information at the time of assessment without reasonable justification. If you have mitigating circumstances which would allow your results to be seen in a new light you should send these to the Exam Board (via the MSc administration) rather than waiting for an appeal. To appeal, contact the Chair of the Board of Examiners or your Director of Studies. Appeals should be directed to the Senatus Postgraduate Studies Committee.

Appeal Proceedures

IMPORTANT: you will not be allowed to proceed to a project unless you have passed your exams so it is vitally important that the Board of Examiners is aware of any circumstances in advance. Appeals will delay your project unless you get special permission to start in advance of an appeal by the College Postgraduate Studies Committee.

If you have some condition for which you might be granted extra time in examinations (e.g. dyslexia), you should definitely contact the Disability Office in order for you to be assessed by the relevant University panel.  It is also advisable to inform the Course Organiser at the start of the course.  Assessment takes some time, so you should notify us as soon as possible.


The Diploma

For students who wish to leave early (immediately after the exams) or who do not achieve sufficient grades in their exams and coursework to proceed to an MSc project (still at least 40%) the degree of Diploma can be awarded. The Diploma course ends with the exams and there is no project or summer semester work.


Plagiarism

The University has issued guidance on plagiarism

It is a natural and beneficial part of the educational experience for students to discuss their work with each other and to incorporate ideas from many sources into their work. However, there is an important difference between an acceptable use of other people's ideas and copying or sharing other people's work without attribution.

For assessment to be fair, the extent to which submitted work is your own must be clear. You must not plagiarise other people's work, presenting it as your own.

Plagiarism is a serious offence. It is often easy to detect. The School will use a number of detection methods to screen coursework. When plagiarism is detected, penalties appropriate to the problem will be applied, the Head of School will be informed as well as the College of Science and Engineering, and your academic record may be amended permanently.

Deliberately allowing your own work to be copied undermines the assessment process. Where there is collusion between students, all students involved may be penalised or disciplined.

For further guidance on what is, and what is not, acceptable consult:
http://www.informatics.ed.ac.uk/admin/ITO/SchoolGuidelinesPlagiarism.html


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