Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories:
- 12-15 ECTS in technical scientific modules (TSM)
TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
- 9-12 ECTS in fundamental theoretical principles modules (FTP)
FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
- 6-9 ECTS in context modules (CM)
CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.
In the module description (download pdf) you find the entire language information per module divided into the following categories:
Machine learning (ML) emerged out of artificial intelligence and computer science as the academic discipline concerned with “giving computers the ability to learn without being explicitly programmed” (A. Samuel, 1959). Today, it is the methodological driver behind the mega-trend of digitalization. ML experts are highly sought after in industry and academia alike.
This course builds upon basic knowledge in math, programming and analytics/statistics as is typically gained in respective undergraduate courses of diverse engineering disciplines. From there, it teaches the foundations of modern machine learning techniques in a way that focuses on practical applicability to real-world problems. The complete process of building a learning system is considered:
- formulating the task at hand as a learning problem;
- extracting useful features from the available data;
- choosing and parameterizing a suitable learning algorithm.
Covered topics include cross-cutting concerns like ML system design and debugging (how to get intuition into learned models and results) as well as feature engineering; covered algorithms include (amongst others) Support Vector Machines (SVM) and ensemble methods.
- Math: basic calculus / linear algebra / probability calculus (e.g., derivatives, matrix multiplication, normal distribution)
- Statistics: basic descriptive statistics (e.g., mean, variance, co-variance, histograms, box plots)
- Programming: good command of any structured programming language (e.g., Python, Matlab, R, Java, C, C++)
- Analytics: basic data analysis methods (data pre-processing, linear & logistic regression)
- Students know the background and taxonomy of machine learning methods
- On this basis, they formulate given problems as learning tasks and select a proper learning method
- Students are able to convert a data set into a proper feature set fitting for a task at hand
- They evaluate the chosen approach in a structured way using proper design of experiment
- Students know how to select models, and „debug“ features and learning algorithms if results do not fit expectations
- Students are able to leverage on the evaluation framework to tune the parameters of a given system and optimize its performances
- Students have seen examples of different data sources / problem types and are able to acquire additional expert knowledge from the scientific literature
Contents of Module
- Introduction (2 weeks): Convergence for participants with different backgrounds
- Supervised learning (7 weeks): Learn from labeled data
Cross-cutting topics: Feature engineering; ensemble learning; debugging ML systems
Algorithms: e.g. SVM, ensemble learning, graphical models (Bayesian networks)
- Unsupervised learning (3 weeks): Learning without labels
Algorithms: e.g., dimensionality reduction, anomaly detection, archetypal analysis
- Special chapters (2 weeks):
Algorithms: e.g., reinforcement learning, recommender systems, hidden Markov / Gaussian mixture models
Teaching and Learning Methods
Classroom teaching; programming exercises (e.g., in Python 3)
T. Mitchell, “Machine Learning”, 1997
C. M. Bishop, “Pattern Recognition and Machine Learning”, 2006
G. James et al., “An Introduction to Statistical Learning”, 2014
K. Murphy, “Machine Learning – A Probabilistic Perspective”, 2012