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M.Sc.|MBA|Micro Degree BDAI_machinelearning
Machine Learning and Analytics

Big data drives decision-making with machine learning and data analytics. This course explores sub-symbolic systems like regression, discrimination, decision trees, neural networks, and SVMs. Students learn data harmonization, pipelines, and AI problem-solving strategies.

  1. Course Start:

  2. Course End:

  3. Estimated Effort:

    10:00 h/week
Book Micro-Degree Program Enrollment in this course is by invitation only*
  • *This course is subject to a fee. Please, book the course using the “Book Micro-Degree Program” button. If you have not registered at German-UDS.academy yet, please register with the same email address that you have used for the booking. If you already have an account at German-UDS.academy, please use the same email address for the booking system.

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 in odd quarters and can, therefore1, be selected as an elective in the following study programs:

  • M.Sc. Cybersecurity
  • M.Sc. Advanced Digital Reality
  • M.Sc. Digital Leadership
  • MBA Digital Technologies
  • MBA Digital Transformation

1: Generally all mandatory modules of a study program can be selected as an elective in all study programs in which they are not offered as a mandatory module. However, there are several rules that apply. First, at least half of the selected electives have to be taken from your study program. Second, all electives are only available in either odd or even quarters. Therefore, not all electives are available in all programs (given that you want to finish the program within the regular time frame and do not want to take that elective in parallel to your impact project.)

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.

Requirements

None

General Information

  • Teaching Format: Experience
  • Total Workload Master: 125h (40h/85h) / 5 ECTS
  • Total Workload MBA: 100h (40h/60h) / 4 ECTS
  • Total Workload Micro Degree: 125h (40h/85h) / Equivalent to 5 ECTS
  • Module coordinator: Dr. Felix Weitkämper
  • Examinations: Quizzes, presentation(s), essay(s)/paper(s), project report(s), written exam (tbd) - Details will be announced with course start.
  • Offered: Odd quarters

Course Staff

Dr. Felix Weitkämper

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 is part of the German UDS family since 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.

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