Modulbeschreibung

Machine Learning and Data Science (EEU)

Kurzzeichen:

M_MLDS

Unterrichtssprache:

Englisch

ECTS-Credits:

Leitidee:

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

Modulverantwortung:

Prof. Dr. Nordborg Henrik

Standort (angeboten):

Rapperswil-Jona

Modultyp:

Wahlpflicht-Modul für Erneuerbare Energien und Umwelttechnik STD_21(Empfohlenes Semester: 5)Kategorie:Spezialkategorie: Grundlagen EEU, Vertiefung ET, Vertiefung UT, Mathematik, Naturwissenschaften (EEU-eeumn)

Wahlpflicht-Modul für Erneuerbare Energien und Umwelttechnik STD_24(Empfohlenes Semester: 5)Kategorie:Vertiefungsmodule EEU (EEU-VT)

Modulbewertung:

Note von 1 - 6

Leistungsnachweise und deren Gewichtung

Modulschlussprüfung:

Mündliche Prüfung, 30 Minuten

Während der Unterrichtsphase:

Seminararbeit

Bewertungsart:

Note von 1 - 6

Gewichtung:

Seminararbeit (50%), mündliche Prüfung (50%)

Bemerkungen:

Inhalte

Angestrebte Lernergebnisse (Abschlusskompetenzen):

know the different approaches to AI-based data analysis (e.g. supervised vs. unsupervised learning)
process and analyze data with Python
formulate adequate models for problems from EU and UT and train them on specific data sets
understand the training process as an optimization problem, solve it and evaluate the quality of the result qualitatively and quantitatively
solve technical problems in mixed teams using AI-based data analysis

Modul- und Lerninhalt:

(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

Lehrmittel/-materialien:

Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H.-T. (2012). Learning From Data. AMLBook.
Bishop, C. M. (2016). Pattern Recognition and Machine Learning (Softcover reprint of the original 1st edition 2006 (corrected at 8th printing 2009)). Springer New York.
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. http://www.gaussianprocess.org/gpml/chapters
Brunton, S. L., & Kutz, J. N. (2022). Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (2nd ed.). Cambridge University Press. https://doi.org/10.1017/9781009089517
Peters, J., Janzing, D., & Schölkopf, B. (2017). Elements of causal inference: Foundations and learning algorithms. MIT Press.
Särkkä, S. (2013). Bayesian Filtering and Smoothing. Cambridge University Press. https://users.aalto.fi/~ssarkka/#books