Artificial Intelligence, _MSc

Semester 1

Compulsory Courses

Symbolic AI (CS502K)

15 Credit Points

This course presents the fundamental techniques of Artificial Intelligence, used in system such as Google Maps, Siri, IBM Watson, as well as industrial automation systems, and which are core to emerging products such as self-driving vehicles. This course will equip the student to understand how such AI technologies operate, their implementation details, and how to use them effectively. This course therefore provides the building blocks necessary for understanding and using AI techniques and methodologies.

Detailed course information

Machine Learning (CS5062)

15 Credit Points

This course will deliver the most sophisticated Machine Learning methodologies and algorithms which would be illustrated across a wide range of applications including but not limited to images, videos, health, time series data, language processing, etc. This course provides students with the Machine Learning principles for continuing learning and working in the area of Data Science and Artificial Intelligence.

Detailed course information

Evaluation of AI Systems (CS5063)

15 Credit Points

Artificial intelligence has helped solve complex practical problems such as driving a car, translating text from/to different languages, understanding and answering questions, and playing games such as chess and Go. This course will provide students of our MSc in AI with knowledge of core evaluation concepts, approaches, tools, techniques and technologies.

Detailed course information

Optional Courses

Plus one of the following options:

Applied Artificial Intelligence (CS5079)

15 Credit Points

This course will allow students to use cutting-edge AI technologies to investigate the creation and application of AI systems. Such tools include deep learning libraries and simulation environments.

Detailed course information

AI and Data: Ethical and Legal Considerations (PH5065)

15 Credit Points

This course will introduce and investigate a number of legal and ethical issues around the ethics of technology, particularly around the ethics of artificial intelligence. We will address questions such as the moral status of artificial agents; the difference, if any, between human rights and artificial rights, problems of data bias. We will also consider the question of responsibility in this arena and review regulatory frameworks. This course would be of interest to students from computer science, philosophy, law and health sciences.

Detailed course information

Semester 2

See how the courses are scheduled in our September and January start programmes in this infographic.

Compulsory Courses

Knowledge Representation and Reasoning (CS551J)

15 Credit Points

Recent advances in AI have changed the perception of what machines can do, from on-line search to answering questions. An underlying feature of many AI systems concern how knowledge is acquired, represented, and reasoned with. Companies such as Google, IBM, and Facebook have been developing sophisticated tools for knowledge representation and reasoning. This module provides the theory and practice of knowledge representation and reasoning, also presenting cutting-edge technologies, libraries and tools. At the end of the course students will be able to design, implement and evaluate knowledge-intensive AI systems.

Detailed course information

Software Agents and Multi - Agent Systems (CS551K)

15 Credit Points

The global autonomous systems market is expected to be valued at over £13 billion by 2025, involving both software systems and robots. Such autonomous systems act to achieve goals with no human intervention, and are already found in Tesla's self-driving cars, NASA space probes and systems such as Amazon's Echo. This course provides the student with a solid grounding in the theory and tools which underpin such systems, teaching them both how to develop such systems, and use them effectively as part of a larger product.

Detailed course information

Data Mining with Deep Learning (CS552J)

15 Credit Points

This course aims to make students familiar with basic data mining and visualisation techniques and software tools. Students will learn how to analyse complex datasets by applying data pre-processing, exploration, clustering and classification, time series analysis, and many other techniques. This course will also cover text mining and qualitative modelling. Through this course students will be able to analyse real-world datasets in various domains and discover novel patterns from them. This course is particularly suitable for those who are interested in working as data analysts or data scientists in the future.

Detailed course information

Natural Language Generation (CS551H)

15 Credit Points

The aim of the course is to introduce students who have some background in computing to (1) the varied aims for which Natural Language Generation (NLG) is pursued, (2) the main rule based and statistical methods that are used in NLG, and (3) some of the main NLG algorithms and systems. The course will cover NLG both as a theoretical enterprise (e.g., for constructing models of language production) and as practical language engineering, paying particular attention to the link between NLG and data science. Some programming experience is expected.

Detailed course information

Semester 3

See how the courses are scheduled in our September and January start programmes in this infographic.

Compulsory Courses

MSc Project in Artificial Intelligence (CS5917)

60 Credit Points

This course will provide students of our MSc in AI programme with the opportunity to develop their own AI research project, under the supervision of a member of staff. Typical projects include extending, improving or adapting existing AI theories or techniques to solve different problems, comparing competing techniques or tools to solve a particular problem, and so on. Students will improve their problem-solving and communication skills, as well as broaden, deepen and consolidate knowledge obtained in other components of the degree.

Detailed course information

Programme Fees

Fee information

Fee category	Cost
EU / International students	£27,000
Tuition Fees for 2023/24 Academic Year
Home / RUK	£11,845
Tuition Fees for 2023/24 Academic Year

Semester 1

Compulsory Courses

Getting Started at the University of Aberdeen (PD5506)

This course, which is prescribed for all taught postgraduate students, is studied entirely online, takes approximately 5-6 hours to complete and can be taken in one sitting, or spread across a number of weeks.

Topics include orientation overview, equality and diversity, health, safety and cyber security and how to make the most of your time at university in relation to careers and employability.

Successful completion of this course will be recorded on your Enhanced Transcript as ‘Achieved’.

Detailed course information

Data Mining with Deep Learning (CS552J)

15 Credit Points

This course aims to make students familiar with basic data mining and visualisation techniques and software tools. Students will learn how to analyse complex datasets by applying data pre-processing, exploration, clustering and classification, time series analysis, and many other techniques. This course will also cover text mining and qualitative modelling. Through this course students will be able to analyse real-world datasets in various domains and discover novel patterns from them. This course is particularly suitable for those who are interested in working as data analysts or data scientists in the future.

Detailed course information

Natural Language Generation (CS551H)

15 Credit Points

The aim of the course is to introduce students who have some background in computing to (1) the varied aims for which Natural Language Generation (NLG) is pursued, (2) the main rule based and statistical methods that are used in NLG, and (3) some of the main NLG algorithms and systems. The course will cover NLG both as a theoretical enterprise (e.g., for constructing models of language production) and as practical language engineering, paying particular attention to the link between NLG and data science. Some programming experience is expected.

Detailed course information

Knowledge Representation and Reasoning (CS551J)

15 Credit Points

Recent advances in AI have changed the perception of what machines can do, from on-line search to answering questions. An underlying feature of many AI systems concern how knowledge is acquired, represented, and reasoned with. Companies such as Google, IBM, and Facebook have been developing sophisticated tools for knowledge representation and reasoning. This module provides the theory and practice of knowledge representation and reasoning, also presenting cutting-edge technologies, libraries and tools. At the end of the course students will be able to design, implement and evaluate knowledge-intensive AI systems.

Detailed course information

Software Agents and Multi - Agent Systems (CS551K)

15 Credit Points

The global autonomous systems market is expected to be valued at over £13 billion by 2025, involving both software systems and robots. Such autonomous systems act to achieve goals with no human intervention, and are already found in Tesla's self-driving cars, NASA space probes and systems such as Amazon's Echo. This course provides the student with a solid grounding in the theory and tools which underpin such systems, teaching them both how to develop such systems, and use them effectively as part of a larger product.

Detailed course information

Semester 2

Compulsory Courses

Symbolic AI (CS502K)

15 Credit Points

This course presents the fundamental techniques of Artificial Intelligence, used in system such as Google Maps, Siri, IBM Watson, as well as industrial automation systems, and which are core to emerging products such as self-driving vehicles. This course will equip the student to understand how such AI technologies operate, their implementation details, and how to use them effectively. This course therefore provides the building blocks necessary for understanding and using AI techniques and methodologies.

Detailed course information

Evaluation of AI Systems (CS5063)

15 Credit Points

One of the biggest challenges in Artificial Intelligence is evaluating how well AI systems work. This course will provide students of our MSc in AI with knowledge of core evaluation concepts, approaches, tools, techniques and technologies; we will also look at software testing of AI systems.

Detailed course information

Machine Learning (CS5062)

15 Credit Points

This course will deliver the most sophisticated Machine Learning methodologies and algorithms which would be illustrated across a wide range of applications including but not limited to images, videos, health, time series data, language processing, etc. This course provides students with the Machine Learning principles for continuing learning and working in the area of Data Science and Artificial Intelligence.

Detailed course information

Optional Courses

Applied Artificial Intelligence (CS5079)

15 Credit Points

This course will allow students to use cutting-edge AI technologies to investigate the creation and application of AI systems. Such tools include deep learning libraries and simulation environments.

Detailed course information

AI and Data: Ethical and Legal Considerations (PH5065)

15 Credit Points

This course will introduce and investigate a number of legal and ethical issues around the ethics of technology, particularly around the ethics of artificial intelligence. We will address questions such as the moral status of artificial agents; the difference, if any, between human rights and artificial rights, problems of data bias. We will also consider the question of responsibility in this arena and review regulatory frameworks. This course would be of interest to students from computer science, philosophy, law and health sciences.

Detailed course information

Semester 3

Compulsory Courses

MSc Project in Artificial Intelligence (CS5917)

60 Credit Points

This course will provide students of our MSc in AI programme with the opportunity to develop their own AI research project, under the supervision of a member of staff. Typical projects include extending, improving or adapting existing AI theories or techniques to solve different problems, comparing competing techniques or tools to solve a particular problem, and so on. Students will improve their problem-solving and communication skills, as well as broaden, deepen and consolidate knowledge obtained in other components of the degree.

Detailed course information

Programme Fees

Fee information

Fee category	Cost
EU / International students	£27,000
Tuition Fees for 2023/24 Academic Year
Home / RUK	£11,845
Tuition Fees for 2023/24 Academic Year