EC1 - The holder of thedegreepossesses general scientific and technological application-oriented knowledge of the basic concepts, structures and coherence of the specific domain.

1.2 The student knows the key concepts (fundamental definitions, formulas and properties) from algebra, analysis, numerical mathematics and statistics.

Can explain the fundamental mathematical and statistical concepts underlying machine learning algorithms.

1.12 The student knows the key aspects of research methodology and project-based working.

Can plan, execute, and monitor a machine learning project according to the principles of project management.

EC2 - The holder of thedegreepossesses general scientific and discipline-related engineering-technical insight in the basic concepts, methods, conceptual frameworks and interdependent relations of the specific domain.

2.2 The student has insight into the key concepts (fundamental definitions, formulas and properties) from algebra, analysis, numerical mathematics and statistics.

• Can compare different machine learning approaches and justify the selection of a suitable method for a given problem.
• Can explain the machine learning training process and interpret relevant performance metrics to assess model performance and learning behaviour.
• Can critically assess the strengths, limitations, and risks associated with machine learning solutions.
• Can explain the working principles of advanced deep learning architectures and their applicability to different machine learning tasks.

EC3 - The holder of thedegreeis able to recognize problems independently and can take initiative to plan activities and perform accordingly.

3.1 The student can formulate a relevant research question.

Can formulate a research question and corresponding objectives from a dataset and its application context.

EC4 - The holder of thedegreecan gather and obtain relevant scientific and/or technical information and/or he/she can measure the necessary information efficiently and conscientiously. Additionally, he/she can make correct references to information.

4.1 The student can look up scientific and/or technical information in a goal-oriented manner.

• Can gather and analyze relevant background information, stakeholder requirements, and domain knowledge to define and contextualize a machine learning use case.
• Can identify, interpret, and use relevant documentation of machine learning libraries and frameworks.

EC7 - The holder of thedegreecan use the selected methods and tools innovatively to systematically implement domain-specific solutions and designs while being aware of practical and economic conditions and company-related implications.

7.3 The student can write correct and qualitative code using an appropriate development, testing and maintenance strategy.

Can implement, validate, and document a machine learning pipeline in Python using tools such as Scikit-learn, PyTorch, and Google Colab, following appropriate development, testing, and maintenance practices.

EC8 - The holder of thedegreecan interpret (incomplete) results, can deal with uncertainties and constraints and can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly.

8.2 The student can reflect critically on a technical-scientific project.

Can formulate a critical self-reflection.

8.3 The student can adjust his own thinking and actions through critical reflection.

Is able to refine the proposed solutions based on critical reflection, qualitative and quantitative data, and feedback.

EC9 - The holder of thedegreecan communicate with colleagues in oral and in written form (including in a graphical way) about domain-specific aspects in suited language making use of apt terminology.

9.1 The student is able to communicate in writing in a correct, structured and appropriate manner in languages relevant to their field of study.

• Can effectively report on and document the selected solutions.
• Can critically reflect on their own work and decision-making process.

9.2 The student can communicate orally in a correct, structured and appropriate way in languages relevant to his field of study.

Can present and defend project objectives, methods, results, and conclusions in a clear, structured, and professional manner.

9.3 The student can communicate in a correct, structured and appropriate graphical way.

Can present, interpret, and compare machine learning results using clear and appropriate visualizations.

EC11 - The holder of thedegreeis able to think and act responsibly realising a project taking into account social and international values, relations and consequences.

11.4 The student has an eye for and takes into account generally accepted social and ethical standards in his own thinking and actions.

Can critically assess ethical, societal, and sustainability aspects of machine learning solutions, including issues related to bias, fairness, privacy, and environmental impact.

EC12 - The holder of thedegreecan act application-oriented and goal-driven and can act academically and professionally with the necessary perseverance and with eye for realism and efficiency, showing a research-oriented attitude towards lifelong learning.

12.1 The student has an open attitude to learn from experience, feedback and mistakes.

Can reflect on feedback received from the teaching staff and use it to improve their work.