Modulbeschreibung

Machine Learning and Data Science (EEU)

ECTS-Credits:

4

Lernziele:

The students learn

the fundamentals of Machine Learning (ML) for data analysis
data analysis and data processing in Python
the application of Machine Learning problems in energy and environmental technology.
to view the learning problem as an optimization process, to solve it, and to evaluate the quality of the results
to solve technical problems in mixed teams using ML-based data analysis

Kurse in diesem Modul

Machine Learning and Data Science:

(Note: not in chronological order)
- Introduction to data analysis with Python
- Review of probability (joint, marginal, and conditional probability; Baye's rule; probability density, ...)
- Review of linear algebra (matrices and vectors, eigen-values and -vectors, factorization, ...)
- Fundamentals of ML-base data analysis
  - Formulation of the learning problem
- Types of learning according to data availability and type
  - supervised learning: regression and classification
  - unsupervised learning: clustering and dimensionality reduction
  - reinforcement learning
  - semi-supervised learning
  - causal learning
- Methods
  - Constrained and regularized optimization
  - Linear and non-linear regression
  - Gaussian processes and support vector machines
  - Neural networks
  - Kalman filter and reservoir computing (iterative learning)
  - Q-learning
  - K-means
  - PCA, ICA, and extensions (e.g. kernel PCA)
- Applications in the fields of renewables and environmental technology
- Other relevant topics
  - The relation between ML and AI
  - Interpolation, Extrapolation
  - Filtering, smoothing, and forecasting
  - Model selection Overfiting and classes of functions

Vorlesung mit 2 Lektionen pro Woche

Uebung mit 2 Lektionen pro Woche

Disclaimer

Diese Beschreibung ist rechtlich nicht verbindlich! Weitere Informationen finden Sie in der detaillierten Modulbeschreibung.