Master's in Artificial Intelligence

Artificial Intelligence

Overview: A detailed look at the historical developments of AI. You'll get to grips with its various successes and setbacks. Learn about expert systems in early AI.
Skills: Study the history of AI and its application areas. Understand expert systems and neuroscience.
After completing the course, you'll be able to:

• Remember the history of artificial intelligence. Understand modern developments in artificial intelligence.

• Analyse the different application areas of artificial intelligence.

• Apply Prolog to simple expert systems.

• Comprehend the brain and cognitive processes from a neuroscientific point of view.

  
  

Advanced Mathematics

Overview: Develop the advanced mathematics foundations required for data science. Use calculus and linear algebra to optimise data structures.
Skills: Understand and manipulate mathematical models used in algorithm design and predictive analytics.
After completing the course, you'll be able to:

• Solve mathematical problems using linear algebra and differential calculus.

• Apply optimisation techniques to real-world data challenges.

• Translate mathematical concepts into computational models.

• Use formal notation to communicate statistical reasoning.

Advanced Statistics

Overview: Explore the statistical principles that power modern analysis of data. Learn about the roles of distributions and regression. Hypothesis testing is another key aspect of the module.
Skills: Design, test, and evaluate statistical models for structured and unstructured data sets.
After completing the course, you'll be able to:

• Conduct data analysis using appropriate statistical methods.

• Interpret and validate statistical findings in practical contexts.

• Develop confidence intervals and test hypotheses.

• Understand data variability and sampling methods.

Programming with Python

Overview: Learn how to use Python, one of the most versatile and popular scripting languages. Explore its numerous features and applications with data structures.
Skills: Code using Python effectively within the fields of data science and artificial intelligence.
After completing the course, you'll be able to:

• Remember basic Python syntax and programming concepts. Analyse and apply different methods for error handling in Python.

• Understand data variability and sampling methods.

• Know common and important Python libraries and how to apply them to given programming tasks.

• Understand concepts like environments and version control.

Machine Learning

Overview: Delve into key techniques such as supervised/unsupervised and reinforcement learning.
Skills: Build, train, and evaluate predictive models using Python-based libraries and real-world data sets.
After completing the course, you'll be able to:

• Know the differences between learning algorithms.

• Evaluate model performance using cross-validation and error metrics.

• Apply models to real-world challenges like recommendation systems or churn prediction.

Deep Learning

Overview: An exploration of how the field of data science has been revolutionised in recent years, and how modules built on these techniques have reached or surpassed human performance in many specialised systems and applications. Students will get a thorough understanding of learning models.
Skills: Understand the most common neural network architectures and how they can be trained using dedicated data samples.
After completing the course, you'll be able to:

• Understand the key building blocks of neural networks and the concepts in deep learning.

• Create deep learning models.

• Use alternative methods to train deep learning models.

Software Engineering for Data Intensive Sciences

Overview: An exploration of the shift from more traditional software development approaches to the DevOps culture.
Skills: Use Scrum and Kanban to produce high-quality code.
After completing the course, you'll be able to:

• Understand the agile approaches of Scrum and Kanban. Explain how DevOps brings software development and operations together in one team.

• Write high-quality code using relevant software development techniques and evaluate the requirements for APIs.

• Create APIs for software applications.

• Identify the challenges of bringing a model into production.

UI/UX Expert

Overview: You will conduct user testing for a given interface and work on developing improvements. The work process and the results will become part of your portfolio.
Skills: Use the UX design process to understand how human minds work.
After completing the course, you'll be able to:

• Explain the UX design process and the user-centred mindset.

• Advocate the importance of UX design for IT projects.

• Describe the basic methods of user research and testing.

Master's Thesis

Overview: Conduct a comprehensive independent project that demonstrates your ability to develop original, research-led solutions using what you've learned in this course.
Skills: Carrying a full project lifecycle to its conclusion: from question design to data collection, analysis, and interpretation.
After completing the course, you'll be able to:

• Work on a problem from your major field of study by applying the specialist and methodological skillset you have acquired during your studies.

• Analyse selected tasks with scientific methods, critically evaluate them, and develop appropriate solutions under the guidance of an academic supervisor.

• Record and analyse existing (research) literature appropriate to the topic of the Master's thesis.

• Prepare a detailed written elaboration in compliance with scientific methods.

Overview: An introduction to the knowledge and competencies needed for solving IT management problems. A look at key considerations, such as cost management and human factors.

Skills: Use analytical thinking to organise projects successfully.
After completing the course, you'll be able to:

• Set up different IT project formats.

• Integrate different project plans.

• Lead an IT team and its core/extended members.

Overview: Explore modern computer networks and language processing techniques.

Skills: Study privacy and security issues in this field.
After completing the course, you'll be able to:

• Name important problems in natural language and image processing. Recognise the common algorithms and methods to address said problems.

• Understand common use-case scenarios in which NLP and computer vision techniques are applied.

• Analyse the advantages and drawbacks of various NLP and computer vision algorithms.