BSc (Hons) Artificial Intelligence with Foundation Year

• Foundations of learning
  In this module, students will learn key learning skills such as how people learn in different ways, how to use communication skills effectively within teams and groups, time management, and setting targets and goals.
• Academic integrity and writing for business
  Students will learn about the demands of academic work and how to use sources acceptably. They will learn key skills such as referencing, avoiding plagiarism, developing written communication for business, and conducting research using a range of sources.
• Personal career plan
  This module helps prepare students for their future careers and career planning. It enables learners to identify their core strengths and competencies, understand different job search processes, and how to build and maintain their personal brand.
• Elective: Foundation Year: (see Electives: Foundation Year)
• Applied learning strategies
  This module continues to develop learning strategies to support learners on their educational journey. Students will learn about self-study skills and develop their communication and presentation skills, along with the ability to reflect and deal with challenges and setbacks.
• Maths: foundations – tech related track
  This module revisits essential math concepts from earlier education levels, vital for success in advanced math and bachelor courses. It introduces students to the proper English language for describing and solving math problems.
• Digital skills – tech related track
  This module explores how digital changes are impacting professions and societal structures. It covers significant digital skills and topics like cloud computing, quantum computing, and how digital transformation affects economies, societies, and work practices.
• Project: innovation and investigation
  A final module providing students an opportunity to combine all their learnings from earlier modules in the form of a project from their subject area.

• Introduction to artificial intelligence
  This module gives a thorough overview of the historical developments, successes, and setbacks in AI. It covers reinforcement learning, natural language processing (NLP), and computer vision.
• Introduction to programming with Python
  Provides foundational understanding of the Python programming language, covering syntax, data types, functions, error handling, and important libraries for data science.
• Mathematics: analysis
  This module introduces tools of differential and integral calculus and their interrelations, including generalised differential calculus for multidimensional spaces.
• Statistics – probability and descriptive statistics
  Introduces the fundamentals of probability, Bayesian statistics, random variables, probability density distributions, and important inequality equations and limit theorems.
• Collaborative work
  This module helps students build up essential interdisciplinary competencies for constructive cooperation with others. It covers methods of professional networking and various communication techniques.
• Fundamentals of data protection and cyber security
  Familiarises students with important concepts of IT security, including legal regulations, suitable measures for implementation, and strategies for IT security management.
• Mathematics: linear algebra
  Introduces the foundations of linear algebra, exploring concepts such as vectors, matrices, and linear equations.
• Statistics – inferential statistics
  This module provides an in-depth look at point estimators, Bayesian statistics, statistical testing, decision theory, and techniques for statistical analysis in practical applications.

• Introduction to academic work for IT and technology
  This module teaches the basics of scientific thinking and working, handling sources, and writing academic papers. It uses examples from IT and technology to illustrate these points.
• Object-oriented and functional programming with Python
  Covers advanced Python programming concepts including classes, objects, design principles, functional programming, and related libraries.
• Machine learning: supervised learning
  Provides an introduction to supervised learning, covering models in regression, classification, large margin classifiers, and tree-structured models.
• Machine learning: unsupervised learning and feature engineering
  Covers unsupervised learning and feature engineering, including clustering models, dimensionality reduction, feature extraction, and ethical implications of machine learning.
• Introduction to NLP
  Teaches traditional and state-of-the-art NLP techniques, challenges, and solutions, including algorithms used in different application scenarios.
• Project: AI excellence with creative prompting techniques
  Students engage in hands-on exercises to create AI-generated content using basic prompting techniques, applying ethical considerations and solving complex problems in their field of study.
• Elective A (see Electives A section below)
• Elective A (see Electives A section below)

• Neural nets and deep learning Discusses neural networks and deep learning, covering feed-forward networks, convolutional and recurrent neural networks, and avoiding overtraining. Additionally, it looks at the impact of model design and data selection on algorithmic fairness.
• Seminar: ethical considerations in data science Students critically analyse ethical considerations in data science, assessing the impacts on both personal and societal levels and presenting their findings in a final scientific paper.
• Elective B (see Electives B section below)
• Elective B (see Electives B section below)
• Elective C (see Electives C section below)
• Elective C (see Electives C section below)
• Undergraduate (bachelor) thesis
  Apply the subject-specific and methodological competencies learned throughout your course to present an academic dissertation. You’ll also learn how to tackle a practical-empirical or theoretical-scientific problem.

• Foundations of academic English
  Introduces correct and concise writing by reviewing language and grammar, paragraph structure, punctuation, and academic integrity. This elective is compulsory for international students.
• Skills for business Learn essential business skills, including writing business emails and proposals, giving presentations, and understanding company structures.
• Developing a business mindset*
  Learners explore key skills and attributes required to develop a business mindset, including managing self, learning from failure, dealing with DEI (Diversity, Equality, Inclusion), and understanding financial literacy at a personal level.
• Business ethics
  This module involves applying ethical principles to business practices with a focus on social justice and sustainability. It provides guidance on ethical decision-making, enabling students to develop informed moral judgments.

*Please note, this elective will be available to study from April 2025.

• Intercultural and ethical decision-making
  Gain the skills to understand intercultural competencies, diversity, and ethics development. Plan and implement learning processes in these areas by creating a case study.
• Project: edge AI
  Whether it’s for vehicles, mobile systems, or cyber-physical systems, you’ll learn to assess requirements, make design choices, and check the performance of your AI system. You’ll create a prototype of your Edge AI solution, reporting on your design and implementation choices, requirements and constraints.
• Data science software engineering
  This module gives a detailed overview of data science methods and paradigms to develop enterprise-grade models and bring them into production. This module explores traditional and agile project management techniques and software development paradigms, including pair, mob, and extreme programming.
• User experience
  Get a solid understanding of the field of user experience by defining terms and concepts, charting customer journeys, designing personas, and applying suitable techniques for UX design.
• Introduction to robotics
  Embark on a journey through the ever-evolving field of robotics and how they are used in real applications. Critically analyse key developments, understand the mechanical structure of robots, and dive into specific characteristics and challenges of industrial and mobile robots.
• Introduction to computer vision
  Learn the foundational elements of computer vision, how to apply deep learning methods to high-level vision tasks and how to handle video processing effectively. Understand image acquisition from human and technical perspectives and the role of filtering in image processing.
• Self-arranged internship I (*) Develop your practical and analytical skills by doing an internship and improving your employability. In several preparation tutorials, you’ll consider the working environment and the overall goal of your placement.
• Self-arranged internship II (*) As with the first internship, this is an opportunity to apply your skills and knowledge in a business environment. Develop your communication style, problem-solving ability and time management.

*You’ll be responsible for arranging a suitable internship for your field of study. Once arranged, please complete an application to the internship module via myCampus, to make sure your internship can be recognised and credited towards your course requirements.

Data analysis and business intelligence

• Advanced data analysis
  Get up to speed with the many analytics platforms widely used in business today. This module looks at everything from measuring business performance to text mining, from social media analytics to the current trends in experimental design and setup.
• Project: data analysis
  It’s time to put your data analysis techniques and knowledge to the test in the real world. Develop a report as you apply advanced data analysis to simulate work in a professional data science environment.

IT operations and project management

• IT service management
  Learn about the IT services that keep IT systems running. This module builds knowledge and expertise to provide IT services to organisations, mainly based on the IT Infrastructure Library (ITIL).
• Project: IT service management
  Take the opportunity to analyse and evaluate certain aspects of IT service management within a project framework. You can apply the theory you have learnt to propose practical solutions to company-specific situations.

International marketing and sales

• International marketing
  Learn about strategic marketing in an international context including cultural differences and how they influence international marketing management. Gain insight into:
  • international market research
  • strategic planning
  • and the ethics of international marketing.
• Online marketing
  Examine different types of online marketing such as advertising campaigns and email marketing. You’ll compare channels, consider legal aspects like GDPR and evaluate campaigns with web analytics.

Supply chain management and industry 4.0

• Supply chain management I
  Gain a theoretical and a practical view of supply chain management (SCM). You’ll consider logistical and modern processes, flows and network standards for SCM.
• Supply chain management II
  Learn how to build and maintain a competitive advantage through robust SCM. Analyse strategic activities and instruments in the Plan, Source, Make, Deliver, and Return process categories.

Cloud programming and computing and data engineering

• Cloud programming
  Make your mark in the cloud. As you plan, create, test, and document a scalable cloud service, you’ll get to delve into how a cloud system’s elements are formed and measured. This module also guides you to assess the risks, protective measures, and responsive actions of a cloud service and explore its various use dimensions.
• Cloud computing
  An introduction to cloud computing, its enabling technologies, and analytics capabilities. Learn about cutting-edge advances like serverless computing, storage, and popular cloud offerings.

Production engineering, automation and robotics and autonomous driving

• Production engineering
  Understand the impacts of additive manufacturing and cyber-physical systems and classify manufacturing processes according to DIN 8580. Dive into rapid prototyping, tooling, and direct manufacturing applications along with exploring the elements and properties of cyber-physical production plants.
• Automation and robotics
  Unleash your potential in automation and robotics with an in-depth exploration of automation, measurement control, robot kinematics, and regulation technology. Learn to decipher measurement signals, the role of Boolean algebra in control technology and choose optimal sensors for automation and robotics applications.

Data analysis and business intelligence

• Business intelligence
  This module introduces the procedures and models for data provision, information generation and analysis. Build skills in data warehousing and develop techniques to optimise business activities.
• Project: business intelligence
  This module introduces the procedures and models for data provision, information generation and analysis. Build skills in data warehousing and develop techniques to optimise business activities.

IT operations and project management

• IT project management
  This module provides fundamental IT project management concepts. Learn about project management and organisation, develop cost-management skills, and solve IT project management problems.
• IT architecture management
  This module defines a future blueprint for particular IT developments, including service strategies and available technologies for IT service providers.

International marketing and sales

• Applied sales I
  Globalised demand and intense competition mean that winning customers is increasingly difficult. So, effective sales thinking is vital. Examine key concepts like sales organisation and alternative channels – plus, fine-tune your negotiation skills.
• Applied sales II
  Deepen your knowledge of fundamental sales principles. Explore how customer satisfaction and loyalty contribute to successful sales management and apply your skills in a real-world case study.

Supply chain management and industry 4.0

• Product development in industry 4.0
  In the context of the fourth industrial revolution, you’ll look at the impact of new trends on product development. Consider how alternative approaches to design put the consumer at the centre.
• Project: smart product solutions
  In a practical project framework, showcase how you’ve applied agile engineering methods to smart product solutions. And demonstrate you can choose the right tools to analyse different business models.

Cloud programming and computing and data engineering

• Data engineering
  Explore the important foundational concepts in data engineering: storage infrastructure, data security, systems architecture, developments in storage technology, and the logic of data pipelines.
• Project: data engineering
  Create your own portfolio by working on a real data engineering project. Perhaps you’ll want to set up a Docker container environment. Or how about building a data pipeline according to DataOps principles? The choice is yours.

Production engineering, automation and robotics and autonomous driving

• Self-driving vehicles
  Focus on the safety standards and IT security of autonomous vehicles. There’s a lot to explore – sensor fusion, feature detection, calibration, localisation, satellite-based systems, and motion planning are just a few areas to get you started.
• Seminar: current topics and trends in self-driving technology
  Uncover the most recent developments of autonomous vehicles. Discover the technical advances, philosophical issues, and how it’ll affect law, society and many industries. You’ll then apply your new knowledge in a research essay.