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Master’s in AI (Artificial Intelligence) Online | EQF Level 7

Study Online and take your career to the next level with an EQF Level 7 Master's Degree in AI (Artificial Intelligence)

Taught in English

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EQF Level 7 Master's Degree

Start a new career or upgrade your current position.

4.3

Verified Company

University Level

- 90 EQF Credits

Level 6 degree recommended

2 Years + 3 Months (Self Paced)

Average completion time (flexible + work)

What to expect?

The EQF Level 7 Master's in AI (Artificial Intelligence) is an internationally recognised qualification. This course is ideal for professionals who are motivated to become business analysts OR already work in data and wish to further their qualifications.

Students who study this course will benefit from a top 10 UK computer science programme. The course is 100% online, assignment-based, and has no exams. Lectures are pre-recorded; notes and other study materials will be provided on the provider platform. Which will become available in full at the start of the course.

Additionally, students may benefit from class forums where you can engage with other students on the course or with your course leader.

Accredited by University of Bath

Government of UK logo course is regulated by UK government

This course is accredited by the University of Bath. The University of Bath is a top 150 global university and is recognised internationally. Bath is regulated by Ofqual, an arm of the UK government.

Learning Style

The EQF Level 7 Master's in AI (Artificial Intelligence) is a fully flexible programme. It can be studied full-time, part-time, or as much or little as your schedule allows. Life gets busy, and schedules don't remain the same.

Full-time or part-time degrees allow students to study in manageable 8-week blocks. This course is designed for professionals who need to balance full-time or part-time work with studying when and how it suits them.

  • 24/7 Support: Students can take advantage of the 24/7 technical support services when it suits them.
  • Classes: Feedback is provided by your tutor, guiding you towards improvement.
  • Lectures on demand: Lectures are pre-recorded and available at any time. Take advantage of increased playback speed and rewind features. We also recommend utalising the interactive exercises.
  • Assignment only: Complete 12 assignments and 1 dissertation to gain your certification. 
  • No Exams: This course does not feature any exams and can be done fully remotely.
  • 8-Week Units: This course is operated in blocks of 8 weeks. Each of the 12 units is delivered in order on a fixed schedule. The 8-week per-unit curriculum is equivalent to part-time study.
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Course Requirements

Begin your journey into artificial intelligence with minimal requirements:

  • Age 18+ by the start date of your course.
  • Minimum education requirements of a first or strong second-class bachelor’s (hons) degree or equivalent. 
  • A good proficiency of English in both reading and writing.
  • Access to a computer and internet connection.

If you do not meet the minimum education requirements but exhibit strong professional work experience, an exemption may be granted for admission to the course. Speak with an agent on our live chat.

Tuition and Financing

  • £722 per 5 credits.

Use our flexible payment option to pay just £722 per 5 credits.

  • 14 unit instalments.

Complete your payments over 14 instalments for improved affordability.

  • £12996 total tuition.

For just £12996, students can become level 7 qualified.

Students are required to settle any outstanding fees before progressing to the next unit. Each instalment cycle is 8 weeks and must be paid before commencing. Additionally, the dissertation (30 credits) can be paid in 2 instalments of £2166.

  • Cancel anytime: We don't believe in cancellation fees. Simply cancel before your next payment, and you won't be charged.
  • Zero Hidden Fees: No interest, no sign-up fees, no rubbish. Students never pay more than advertised.
  • Money-back guarantee: Our students can rest assured knowing this course offers a 7-day money-back guarantee.
  • Get Qualified Scheme: The scheme can be used to refund 70% of tuition fees after graduation.
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Get Qualified Scheme FAQs

The Get Qualified Scheme is available for all residents, including both Maltese and non-Maltese residents. 

Yes, the level 7 master’s in AI (artificial intelligence) is eligible for the Get Qualified Scheme refund. Students can locate this course by selecting institution N/A and searching for the awarding body, followed by the course level and title. 

Students who graduate from an eligible course can apply for a 70% refund through the Malta Enterprise online portal. To apply, students must first receive their certificate and then begin the application process.

This programme begins in January, May, and September. The intake deadline is 1 month prior to the start date; however, places are limited and the course may fill up before the deadline. We recommend applying a minimum of 60 days prior to the start date.

More questions?

Our support team can answer any questions related to GQS.

Speak to an agent

Career Outcomes

  • Advance your career with a degree in artificial intelligence.
  • Develop AI skills to underpin your computer science knowledge.
  • Balance computer science and artificial intelligence skill sets to assess and drive growth.
  • Understand different AI models and how they work in practice.
  • In-demand roles:
  • AI Analysts
  • Prompt Engineers
  • Python Developers
  • Computer Scientists
  • Machine Learning Devs
  • Industry benefits:

€30,000 - €55,000 average salary

Artifical intelligence graduates typically secure positions between €30,000 - €55,000.

Access to a wide range of careers

Computer science builds multi-discipline skills that translate to a variety of positions.

12.9% projected growth

Malta's employment is projected to grow 12.9% across sectors by 2027.

Course Modules

12 Modules - 12 Assignments - 1 Dissertation

Topic 1.

What You’ll Learn:

  • Understand problem analysis, design, implementation, and evaluation in AI programming.
  • Explain debugging and testing methods for writing robust AI programs.
  • Implement advanced AI programming concepts using relevant libraries and frameworks.

Skills You’ll Gain: 

Topic 2.

What You’ll Learn:

  • Apply higher mathematics in AI, including calculus, linear algebra, and probability.
  • Formulate mathematical models for AI problems using logical and statistical frameworks.
  • Solve mathematical problems in abstract form and relate them to AI applications.

Skills You’ll Gain: 

Topic 3.

What You’ll Learn:

  • Understand a range of AI techniques, their strengths, and limitations.
  • Apply AI techniques, including search algorithms, Bayesian networks, and neural networks.
  • Critically evaluate state-of-the-art AI applications and their real-world impact.

Skills You’ll Gain: 

Topic 4.

What You’ll Learn:

  • Evaluate AI programming languages and software for data science applications.
  • Implement data science functionality using machine learning techniques.
  • Assess the scalability, performance, and ethical considerations of AI-driven data analysis.

Skills You’ll Gain: 

Topic 5.

What You’ll Learn:

  • Understand design principles for intelligent systems interacting with humans.
  • Evaluate ethical and societal challenges in human-machine collaboration.
  • Conduct research on human-machine interaction and present key findings.

Skills You’ll Gain: 

Topic 6.

What You’ll Learn:

  • Understand fundamental principles and algorithms of NLP.
  • Develop programs for processing and analyzing language-based data.
  • Evaluate NLP models and assess their effectiveness for different applications.

Skills You’ll Gain: 

Topic 7.

What You’ll Learn:

  • Analyse AI system design, architecture, and deployment for real-world applications.
  • Apply software engineering principles to build robust AI-enabled systems.
  • Understand Machine Learning Life Cycle, AutoML, and MLOps for AI production systems.

Skills You’ll Gain: 

Topic 8.

What You’ll Learn:

  • Explore AI techniques such as logic programming, Bayesian networks, and neural networks.
  • Apply AI search algorithms to optimize decision-making processes.
  • Use AI planning tools like PDDL to develop automated problem-solving strategies.

Skills You’ll Gain: 

Topic 9.

What You’ll Learn:

  • Critically evaluate research papers in Computer Science and AI.
  • Develop a structured research proposal, including methodology and ethical considerations.
  • Analyse previous academic work to refine research objectives and approaches.

Skills You’ll Gain: 

Topic 10.

What You’ll Learn:

  • Conduct in-depth research on a chosen AI topic, integrating theory and practical applications.
  • Design, implement, and evaluate AI models for real-world challenges.
  • Write a structured dissertation presenting research findings and future directions.

Skills You’ll Gain: 

Optional Unit Set 1 (General Topics):

Topic 1.

What You’ll Learn:

  • Understand core concepts in supervised, unsupervised, and reinforcement learning.
  • Implement deep learning techniques using neural networks and cloud-based tools.
  • Apply machine learning models to solve real-world business and research problems.

Skills You’ll Gain: 

Topic 2.

What You’ll Learn:

  • Analyse AI technologies in historical, social, and political contexts.
  • Evaluate the ethical and economic implications of AI development and deployment.
  • Assess claims about AI made by governments, media, and tech companies.

Skills You’ll Gain: 

Topic 3.

What You’ll Learn:

  • Understand key components of robotic systems, including sensors and locomotion.
  • Apply image processing techniques to interpret visual data in robotics.
  • Evaluate state-of-the-art robotics applications and ethical considerations.

Skills You’ll Gain: 

Optional Unit Set 2 (Technical Topics):

Topic 1.

What You’ll Learn:

  • Understand fundamental machine learning algorithms and their real-world applications.
  • Implement deep learning techniques such as convolutional neural networks (CNNs).
  • Evaluate machine learning models and their effectiveness in various domains.

Skills You’ll Gain: 

Topic 2.

What You’ll Learn:

  • Differentiate reinforcement learning from traditional supervised learning.
  • Formulate real-world problems as Markov Decision Processes (MDPs).
  • Implement reinforcement learning algorithms for complex decision-making tasks.

Skills You’ll Gain: 

Topic 3.

What You’ll Learn:

  • Understand key techniques in robotic perception and machine vision.
  • Apply SLAM (Simultaneous Localization and Mapping) algorithms for robotic navigation.
  • Evaluate the role of image processing in autonomous robotic systems.

Skills You’ll Gain: 

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