Artificial Intelligence MSc

Program Overview

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The MSc in Artificial Intelligence at Ulster University equips students with the skills for a successful career in the AI industry. The program combines theoretical knowledge with practical experience, focusing on topics such as machine learning, deep learning, robotics, and natural language processing. It is informed by cutting-edge research and industry partnerships, ensuring students are equipped with the most up-to-date knowledge and skills. Graduates are well-placed for careers as AI engineers, data scientists, and researchers in various sectors.

Program Outline

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Degree Overview

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Objectives:

• Equip students with the skills needed for a successful career in the AI industry, with specific focus on knowledge representation, reasoning, robotics, machine learning, deep learning, neural networks, natural language processing, data analytics, and emerging AI topics.
• Prepare students for further research studies in the field of AI.

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Description:

• The MSc in Artificial Intelligence is a postgraduate program designed to address the growing demand for highly skilled professionals in the AI field.
It combines theoretical knowledge with practical experience, allowing students to develop the expertise needed to apply AI techniques to real-world problems.
• The program is underpinned by a multi-million pound infrastructure featuring a large-scale pervasive and mobile computing environment, advanced sensing technology, and rapid prototyping facilities.
• The curriculum is informed by cutting-edge research conducted at the School of Computing, and by strong industry partnerships with BT through the BT Ireland Innovation Centre (BTIIC) and PwC through the Advanced Research and Engineering Centre (ARC).

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Outline

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Content:

• The program comprises six compulsory taught modules (120 credits) and a substantial independent Masters Project (60 credits).
• Modules cover topics such as:
• Data Science and Machine Learning
• Deep Learning and its Applications
• Robotics & AI
• Knowledge Engineering
• Intelligence Engineering and Infrastructure
• Emerging and Advanced Topics in AI

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Structure:

• The program typically requires 15 hours of timetabled sessions per week, including lectures, tutorials, and practical lab work during the taught components.
• The Masters Project takes place in the final semester, separate from the taught modules.
• Students are expected to attend classes and complete independent study, averaging around 36-42 hours of expected learning per week.

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Schedule:

• The program is offered twice a year, with start dates in:
• September 2024
• January 2025

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Individual Modules:

• Data Science and Machine Learning: Provides a comprehensive overview of the data science process, explores mathematical and statistical techniques for data analysis, and equips students with practical programming skills for applying machine learning algorithms to real-world datasets.
• Deep Learning and its Applications: Introduces the fundamentals of deep learning, explores the construction of neural networks and deep learning theory, covers state-of-the-art convolutional neural networks and recurrent neural networks, and provides students with the skills to develop deep learning solutions for computer vision and natural language processing applications.
• Robotics & AI: Offers a comprehensive overview of smart robotics and AI, provides hands-on experience in programming smart robots in the labs, explores practical techniques for coding AI techniques for robotics, and emphasizes the design and implementation of robots exhibiting AI behavior.
• Knowledge Engineering: Covers modern topics in artificial intelligence, including knowledge representation and reasoning (deductive and inductive), and explores their effective utilization in various applications such as decision-making, automated reasoning, formal verification, and the semantic web.
Students will gain in-depth understanding of key concepts and principles and acquire practical skills in critically evaluating and effectively building knowledge-based applications. This includes aspects of machine learning, federated operation of activities, data engineering, tailored computational artifact production, production pipelines, automated testing, and automated deployment.
• Emerging and Advanced Topics in AI: Explores cutting-edge research in the field of artificial intelligence, covers recent advancements in AI theory, algorithms, and applications, and addresses issues such as privacy, fairness, and ethics in AI.
Students will review a range of advanced AI systems and applications, gaining deep understanding of key concepts, principles, and challenges. They will also develop independent learning and research skills, enhance their writing skills, and acquire practical experience in using software to reproduce results from the literature.

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Assessment

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Methods:

• Assessments are 100% coursework based, with no exams.

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Criteria:

• Each module has specific assessment criteria that evaluate students' achievement of learning outcomes.
• Assessment methods typically include essays, reports, seminar papers, tests, presentations, dissertations, designs, artifacts, portfolios, journals, and group work.

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Feedback:

• Timely feedback is provided on all coursework assessments.
• This feedback can be individual or group-based and encourages students to use it for their own development.

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Teaching

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Methods:

• Teaching is delivered through lectures, directed tutorials, seminars, and practical sessions.
• Some sessions may be delivered by industry professionals and researchers.

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Faculty:

• Academic staff in the School of Computing are qualified to teach in higher education and hold relevant postgraduate qualifications.
• Most academic staff (89%) are accredited fellows of the Higher Education Academy (HEA) or above.
• The program is taught by Professors, Readers/Senior Lecturers, and Lecturers.

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Unique Approaches:

• The program features a strong focus on practical applications, with hands-on experience in programming smart robots, building AI systems, and utilizing AI techniques for real-world problems.
• The curriculum is informed by cutting-edge research and industry partnerships, ensuring students are equipped with the most up-to-date knowledge and skills.
• The program provides opportunities to participate in research and industry-related projects through collaborations with BTIIC, CHIC, and ARC.

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Careers

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Career Options:

• The MSc in Artificial Intelligence prepares graduates for a range of career opportunities in the growing AI industry, including:
• AI Engineer
• Machine Learning Engineer
• Data Scientist
• Software Engineer
• Robotics Engineer
• AI Consultant
• AI Researcher
• Graduates are well-placed to work in various industrial settings across sectors such as:
• Health
• Financial Technology
• Advanced Manufacturing
• Media
• Energy
• Civic Society
• Students can participate in industry-related projects through collaborations with BTIIC, CHIC, and ARC.

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Other

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Accreditation:

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Fees:

• Fees for Non-EU/International students are £17,090.00 for the 2024/25 academic year.

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Additional Expenses:

• Students are responsible for additional costs associated with travel, living expenses, graduation ceremonies, examination resits, and library fines.

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Disclaimer:

• The University reserves the right to modify or discontinue the program, or change fees, if necessary.

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Northern Ireland, Republic of Ireland and EU Settlement Status Fees

£7,000.00

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International Fees

£17,090.00