Master of Science in Machine Learning and Artificial Intelligence

In order to register for the programme, one of the following qualifications is required:
• An honours degree in Applied Mathematics, Computer Science, Mathematics, or Mathematical Statistics;
• A four-year bachelor’s degree in Electrical Engineering;
• A qualification deemed equivalent to the above, in a field closely linked to machine learning.

You will also be expected to have existing and demonstrable proficiency in Python or an equivalent programming language, be comfortable with numerical linear algebra and multivariable calculus, and possess basic knowledge of probability theory and statistics.

Final approval for admission rests with the departmental academic committee in collaboration with the programme coordinator, who also take into account the infrastructure and capacity of the Department.

Curriculum vitae (CV)
A document that outlines your educational level, employment history, description of work experience, especially that is relevant to your application to study and contact details of three referees.

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Curriculum vitae (CV)

Motivational letter
One-page document explaining what factors are motivating you to apply for the specific programme(s) and describing what makes you a suitable candidate.

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Motivational letter / Essay

Statement of research interest / Outline
This is a brief summary of the master's research proposal (1-3 pages in length) which outlines your area of research, aims of the research project and the research methodology you plan to use.

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Research project / Outline / Proposal

The programme consists of three separate blocks: compulsory core modules, elective modules and a research project. Every block bears 60 credits, bringing the programme total to 180 credits. The modules in a particular block may not all run in parallel over the entire block; scheduling will depend on intermodular content development and the availability of lecturers.

Programme Content
The programme will equip you with specialist knowledge and skills to the level where you will be able to critically evaluate the suitability of existing theories and techniques for a specific application. The modules (with their associated assignments) and the research project will also develop your abilities to design, select and apply technically advanced methods, techniques and theories to complex practical and theoretical machine-learning and artificial intelligence problems.

Compulsory Modules
Subject number | Module code | Credits | Module Name | Semester

14398 814 15 Applied Machine Learning at Scale 1 or 2
14396 813 15 Foundations of Deep Learning 1 or 2
14394 811 15 Mathematics for Machine Learning 1 or 2
14395 812 15 Probabilistic Modelling and Reasoning 1 or 2
14399 885 60 Research Project (Machine Learning) 1 or 2

Plus Elective Modules
Choose 6 modules to the value of 60 credits. Not all of these modules will necessarily be offered every year.

14404 820 10 Advanced Probabilistic Modelling 1 or 2
14409 825 10 Advanced Topics in Artificial Intelligence 1 or 2
14408 824 10 Advanced Topics in Machine Learning 1 or 2
14407 823 10 Artificial Intelligence and the Brain 1 or 2
62847 842 10 Computer Vision 1 or 2
14406 822 10 Monte Carlo Methods 1 or 2
14401 817 10 Natural Language Processing 1 or 2
14405 821 10 Optimisation for Machine Learning 1 or 2
14402 818 10 Reinforcement Learning and Planning 1 or 2
14403 819 10 Sequence Modelling 1 or 2

Assessment and Examination
• All the modules (except for the research project) will be assessed by means of flexible assessment. This entails a combination of practical assignments and summative assessments.
• All summative assessments will be moderated internally and at least 40% of the final mark will be moderated externally.
• The 60-credit research project will be examined by the supervisor and an independent examiner. A moderator will review the recommendations by the examiner and the supervisor and, if necessary, also examine the project. Either the examiner or the moderator must be external and appointed by the Science Faculty Board.
• To pass the programme, you must obtain at least 50% for the research project and at least 50% for each module.