Awards
Target Award
Award Description:Master of Science - MS
Alternative Exit
Programme Offerings
Full-Time
F2F-JMU-SEP
Educational Aims of the Course
The two principal themes in the programme are the development of machine learning skills relating to data science and deep learning, and the associated software engineering, management and analysis skills required to enact successful enterprise machine learning projects. This is underpinned by themes of computing, networking and software engineering. The main aims are: - To provide students with the technical skills required for the development of enterprise machine learning software solutions. - To enable the student to acquire the skills needed in the investigation of user requirements and the development of a suitable software design using the appropriate specifications and design methodologies. - To prepare students with the management skills required to implement enterprise machine learning. - To provide students with the knowledge of the wide range of issues involved in the implementation of enterprise machine learning. - To encourage students to engage with the development of employability skills by completing a self-awareness statement. - To provide students with a comprehensive understanding, critical awareness and ability to conduct evaluation of enterprise machine learning research issues. - To further develop students' originality in applying analytical, creative, problem solving and research skills. - To provide advanced, conceptual understanding, underpinning career development, innovation and further study such as PhD in the area of enterprise machine learning.
Learning Outcomes
1.
Apply advanced enterprise level machine learning programming to enterprise systems.
2.
Demonstrate the skills necessary to plan, conduct and report a research project.
3.
Specify, design and construct programs to be used for the purpose of machine learning and deployment.
4.
Analyse data and results for a variety of machine learning solutions.
5.
Evaluate different machine learning models and methodologies in terms of general attributes.
6.
Depending upon the task, students to work effectively as individuals or as part of a team.
7.
Identify appropriate tools and techniques to be used for a machine learning problem.
8.
Conduct research into machine learning and related topics.
9.
Use information technology, e.g. Web and internet, for effective information retrieval.
10.
Apply numerical skills to cases involving a quantitative dimension.
11.
Communicate effectively by written or verbal means.
12.
Creatively and effectively use enterprise machine learning development processes.
13.
Plan and manage learning and development.
14.
Engage with complex debates around legal, ethical, social and professional issues regarding Machine Learning.
15.
Critically use algorithms and high performance concepts to solve problems and perform machine learning investigations.
16.
Be innovative when using IT infrastructure: hardware / network configurations, communication, system development tools, developing technologies.
17.
Demonstrate knowledge of machine learning: Traditional and current machine learning models, hosting, deployment, evaluation, and ongoing refinement.
18.
Apply advanced knowledge and demonstrate understanding of facts, concepts, principles and theories relating to machine learning.
19.
Collect and synthesise information from a variety of sources.
20.
Utilise relevant methods and skills to solve well-defined machine learning-based problems.
21.
Critically reflect on the impact of new technologies / standards / legal requirements in the area.
Teaching, Learning and Assessment
Acquisition of 1 - 8 is through a combination of lectures, tutorials, practical sessions and laboratory work. Throughout the learner is encouraged to undertake independent reading both to supplement and consolidate what is being taught/learnt and to broaden their individual knowledge and understanding of the subject. Assessment methods are specified in module specifications. Each module is assessed by one or more pieces of coursework. Specifically the assessment takes the form of laboratory work, coursework reports and presentations. Skills 9 - 13 are taught through lectures and developed through tutorial and lab work throughout the course. Cognitive skills are partly assessed mainly through coursework assessment. The Level 7 projects allow a student to demonstrate his/her cognitive skills. Practical skills 14-18 are developed throughout the programme. Coursework and projects are designed to provide practical opportunities for students to work independently and in groups. Specialist software is available in School labs or from specified PCs in the Learning Resource Centres. Assessment is normally by coursework and projects. Key skills 19-21 are developed throughout the programme in a variety of forms. Specifically through a combination of research related coursework, guided independent study and projects, group work and presentations. Key skills are assessed as part of coursework, projects and presentations
Programme Structure
Programme Structure Description
For an MSc award, students are required to attain 180 credits at Level 7. 120 credits from taught modules, and 60 credits from the project dissertation; For a PG Diploma award, 120 credits of taught modules at Level 7 are required; For a PG Certificate award, 60 credits of taught … For more content click the Read More button below.