(MSc) Master of Science
Artificial Intelligence and its Applications
Current
University of Essex
University of Essex
Computer Science and Electronic Engineering (School of)
Colchester Campus
Masters
Full-time
MSC G412JS
10/05/2023
Details
Professional accreditation
None
Admission criteria
A 2:2 honours degree, or equivalent, in any subject.
IELTS (International English Language Testing System) code
IELTS 6.0 overall with a minimum component score of 5.5
If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.
Additional Notes
The University uses academic selection criteria to determine an applicant’s ability to successfully complete a course at the University of Essex. Where appropriate, we may ask for specific information relating to previous modules studied or work experience.
Course qualifiers
A course qualifier is a bracketed addition to your course title to denote a specialisation or pathway that you have achieved via the completion of specific modules during your course. The
specific module requirements for each qualifier title are noted below. Eligibility for any selected qualifier will be determined by the department and confirmed by the final year Board of
Examiners. If the required modules are not successfully completed, your course title will remain as described above without any bracketed addition. Selection of a course qualifier is
optional and student can register preferences or opt-out via Online Module Enrolment (eNROL).
None
Rules of assessment
Rules of assessment are the rules, principles and frameworks which the University uses to calculate your course progression and final results.
Additional notes
None
External examiners
Dr Colin Johnson
Dr MARJORY CRISTIANY Da COSTA ABREU
Senior Lecturer
Sheffield Hallam University
External Examiners provide an independent overview of our courses, offering their expertise and help towards our continual improvement of course content, teaching, learning, and assessment.
External Examiners are normally academics from other higher education institutions, but may be from the industry, business or the profession as appropriate for the course.
They comment on how well courses align with national standards, and on how well the teaching, learning and assessment methods allow students to develop and demonstrate the relevant knowledge and skills needed to achieve their awards.
External Examiners who are responsible for awards are key members of Boards of Examiners. These boards make decisions about student progression within their course and about whether students can receive their final award.
Programme aims
Designed for students seeking a career that applies AI techniques to solve problems in many different sectors. Our course opens up employment opportunities designing intelligent software – in banks and businesses designing prediction systems, in computer games companies designing adaptive games, in pharmaceutical companies designing intelligent systems that model a given drug and its various interactions, and in heavy industries designing control systems. The course will equip the students with both theoretical and technical relevant skills. The course will also equip the students with transferable skills such as the ability to develop and present arguments, as well as the ability to work independently and in groups. Students will learn about topics including Machine learning, Data mining, Decision making, Intelligent systems, as well as understand the specifics and the intricacies of AI methodologies. They will also be able to apply both basic and advanced AI methods, and design and implement novel solutions.
Learning outcomes and learning, teaching and assessment methods
On successful completion of the programme a graduate should demonstrate knowledge and skills as follows:
A: Knowledge and understanding
A1: A comprehensive understanding of the relevant scientific principles of the specialisation.
A2: A critical awareness of current problems and/or new insights in the AI area.
A3: Understanding of concepts relevant to the discipline, some from outside engineering, and the ability to evaluate them critically and to apply them effectively, including in engineering projects.
Learning methods
Skills A1-A3 are acquired through teaching sessions (lectures, laboratories, classes) and related coursework. the work that students do for their modules. Lectures provide a means for teachers to demonstrate these skills through examples and applications. Preparation for teaching sessions, provides an opportunity for students to develop their knowledge and understanding of the content of the modules. Teachers provide written and/or verbal feedback on student work through comment and discussion. In addition, teachers engage students outside the classroom through academic support hours, appointments, and email.
The usual range of approaches will be taken to ensure that learning and teaching methods are inclusive, including providing clear and well-structured material, uploading it on moodle, offering the teaching sessions for ListenAgain, informing teachers at the start of the term about students with special needs, allowing student voice group representatives to discuss issues surrounding learning for minorities
Assessment methods
Outcomes are assessed throughout the modules comprising the degree by means of written examinations and coursework. The dissertation provides a further opportunity to assess these outcomes.
The usual range of approaches will be taken to ensure that assessment is inclusive, including allowing for extra exam time where necessary, and taking into account extenuating circumstances.
B: Intellectual and cognitive skills
B1: Ability to work with information that may be incomplete or uncertain
B2: Logically assess particular problems in the AI area that arise in possibly unfamiliar situations, choose and apply appropriate methods for their solution among basic tools of analysis.
B3: Exercise critical judgement in assessing different and competing engineering analysis methods for solving complex problems, appraising their merits and assessing their limitations.
B4: Ability to use fundamental knowledge to investigate new and emerging technologies.
Learning methods
Skills B1-B4 are acquired and enhanced primarily through the work that students do for their modules. Teachers provide written and/or verbal feedback on student work through comment and discussion. In addition, teachers engage students outside the classroom through academic support hours, appointments, and email.
The usual range of approaches will be taken to ensure that learning and teaching methods are inclusive, including uploading material on moodle, offering the teaching sessions for ListenAgain, informing teachers at the start of the term about students with special needs, allowing student voice group representatives to discuss issues surrounding learning for minorities.
Assessment methods
Skills B1-B4 are assessed throughout the modules comprising the degree by means of written examinations and coursework, including the MSc dissertation.
The usual range of approaches will be taken to ensure that assessment is inclusive, including allowing for extra exam time where necessary, and taking into account extenuating circumstances.
C: Practical skills
C1: Identify, select and gather research data using relevant sources, including the library and online searches
C2: Develop expertise in programming languages used widely in AI applications, like python
C3: Apply engineering techniques to independently (though with supervision) solve problems in real world situations
C4: Awareness of the need for a high level of professional and ethical conduct in engineering.
C5: Organise ideas in a systematic and critical fashion; in computer code, in mathematical language and in writing.
Learning methods
Skills C1-C5 are acquired and enhanced primarily through the work that students do for their modules, as well as for their dissertation. Student preparation involves the reading, interpretation and evaluation of the relevant material including the relevant literature.
The usual range of approaches will be taken to ensure that learning and teaching methods are inclusive, including uploading material on moodle and offering the teaching sessions for ListenAgain.
Assessment methods
Skills C1-C5 are assessed throughout the modules comprising the degree by means of written examinations and coursework, including the MSc dissertation. Skill C1 is informally assessed by student's preparation for each module.
The usual range of approaches will be taken to ensure that assessment is inclusive, including allowing for extra exam time where necessary, and taking into account extenuating circumstances.
D: Key skills
D1: Communication in writing, using appropriate terminology and technical language
D2: Production of a word-processed coursework. Development of web-skills. Numerical and computational skills using, among others, Python programming language.
D3: Use of mathematical techniques to analyse data
D4: Application of logical reasoning to address issues in real world situations
D5: Understanding of different roles within an engineering team and the ability to exercise initiative and personal responsibility, which may be as a team member or leader.
D6: Capacity to: (a) organise and implement a plan of independent study; (b) reflect on their own learning experience and adapt in response to feedback; and (c) recognise when they need to learn more and appreciate the role of additional research
Learning methods
Students are guided in acquiring skills D1-D6 through lectures, labs and individual advice from academics. These skills are further developed as students pursue the learning activities associated with their modules and in the MSc dissertation. Students also have the opportunity to develop skills in working in groups through their participation in certain module-related activities.
The usual range of approaches will be taken to ensure that learning and teaching methods are inclusive, including uploading material on moodle and offering the teaching sessions for ListenAgain.
Assessment methods
Skills D1-D6 are assessed throughout the modules comprising the degree by means of written examinations and coursework, including the MSc dissertation.
The usual range of approaches will be taken to ensure that assessment is inclusive, including allowing for extra exam time where necessary, and taking into account extenuating circumstances.