Machine Learning and Analytics

About This Course

Decision-making is empowered by (big) data through the use of machine learning and data analytics principles. The course looks at subsymbolic systems: regression models, linear and nonlinear discriminators, decision trees, small neural networks, and support vector machines, among others.

This module conveys fundamental technologies behind big data applications. The students are tasked with understanding and experiencing core principles of e.g. data harmonization and data pipelines fueling machine-learning algorithms.

In the course, students will compare different theoretical approaches to AI and be able to choose the best problem-solving strategy for a given application.

Learning Objectives

Students understand the fundamental goals of machine learning and data analytics.

Provide students with an advanced understanding of the mathematics and technology behind AI models.

They become proficient in the application of methods of data science, machine learning, and data analytics.

Students can identify problems in data science and are prepared to select the best approach from a toolbox of algorithms.

Students are able to formulate a scientific problem-oriented report structuring and explaining their methods of problem-solving and the respective results.

Study Programs

This course is mandatory for the following study programs.

M.Sc. Applied AI

This course is offered as an elective for the following study programs.

MBA Digital Technologies

MBA Digital Transformation

M.Sc. Advanced Digital Reality

M.Sc. Cybersecurity

M.Sc. Digital Leadership

Micro Degree

This course is offered as a micro degree.

German UDS Micro Degrees are compatible with the European MOOC Consortiums Common Micro Credentials Framework.

Micro Degrees will be rewarded with an equivalent of 5 ECTS.

Micro Degrees are offered to non-regular students and require a fee of €900.

General Information

Teaching Format: Experience

Total Workload Master: 125h (40h/85h) / 5 ECTS

Total Workload MBA: 100h (30h/70h) / 4 ECTS

Total Workload Micro Degree: 125h (40h/85h) / Equivalent to 5 ECTS

Module coordinator: tbd

Examinations: Quizzes, presentation(s), essay(s)/paper(s), project report(s), written exam (tbd) - Details will be announced with course start.

Offered: Even quarters

Course Staff

Dr. Felix Weitkämper

Felix has completed his undergraduate education in Mathematics with Philosophy at the LMU in Munich. Afterwards, he completed a doctorate in Mathematics with the Logic Group at the University of Oxford.

After graduating from Oxford, he spent a year teaching 15-19 year olds at a technical college in the North of England as part of the Researchers in Schools initiative, which brought postgraduates from selective universities into schools in disadvantaged parts of England.

In 2020, Felix moved into computer science and joined the programming languages and AI group at the LMU in Munich as a postdoctoral researcher. His research focus lies on interpretable, human-centered AI, and in particular on the combination of statistical learning and modelling with sophisticated logical reasoning. Within this field, his work spans from mathematical foundations to systems and applications in the life sciences.

He will be part of the German UDS family from October 2024 as a full-time senior researcher, doing his part for the new generation of AI specialists and deciders to be grounded in the whole breadth of AI research and technique.