Postgraduate Diploma - PD

Master of Science - MS

Level 7 Core

Level 7 Optional

Level 7

Programme Structure

Applicants should normally have one of the following qualifications: Degree, not necessarily in Computing, when the applicant has relevant skills, or Degree equivalent professional qualifications, e.g. BCS Professional Graduate Diploma in IT. DipHE or HND plus a minimum of 3 years relevant professional experience. Students with non-standard entry qualifications, relevant industry experience or certification are also encouraged to apply. Admission for these candidates will be at the discretion of the Programme Leader. Applicants with non-standard qualifications may be required to submit a CV and references.

Applicants should normally have one of the following qualifications: Degree, not necessarily in Computing, when the applicant has relevant skills, or Degree equivalent professional qualifications, e.g. BCS Professional Graduate Diploma in IT. DipHE or HND plus a minimum of 3 years relevant professional experience. Students with non-standard entry qualifications, relevant industry experience or certification are also encouraged to apply. Admission for these candidates will be at the discretion of the Programme Leader. Applicants with non-standard qualifications may be required to submit a CV and references. Offerings: All

Where candidate's first degree was not taught and assessed in English, a minimum IELTS 6 is required (with a minimum of 5.5 on each component) or equivalent.

Where candidate's first degree was not taught and assessed in English, a minimum IELTS 6 is required (with a minimum of 5.5 on each component) or equivalent. Offerings: All

Use information technology, e.g. Web and internet, for effective information retrieval.

Apply advanced knowledge and demonstrate understanding of facts, concepts, principles and theories relating to machine learning.

Engage with complex debates around legal, ethical, social and professional issues regarding Machine Learning.

Demonstrate knowledge of machine learning: Traditional and current machine learning models, hosting, deployment, evaluation, and ongoing refinement.

Utilise relevant methods and skills to solve well-defined machine learning-based problems.

Apply advanced enterprise level machine learning programming to enterprise systems.

Apply numerical skills to cases involving a quantitative dimension.

Analyse data and results for a variety of machine learning solutions.

Communicate effectively by written or verbal means.

Demonstrate the skills necessary to plan, conduct and report a research project.

Collect and synthesise information from a variety of sources.

Critically use algorithms and high performance concepts to solve problems and perform machine learning investigations.

Conduct research into machine learning and related topics.

Creatively and effectively use enterprise machine learning development processes.

Critically reflect on the impact of new technologies / standards / legal requirements in the area.

Plan and manage learning and development.

Depending upon the task, students to work effectively as individuals or as part of a team.

Identify appropriate tools and techniques to be used for a machine learning problem.

Specify, design and construct programs to be used for the purpose of machine learning and deployment.

Be innovative when using IT infrastructure: hardware / network configurations, communication, system development tools, developing technologies.

Evaluate different machine learning models and methodologies in terms of general attributes.

Course Catalogue

Artificial Intelligence (Machine Learning) - 36223 - MSc