- ZHAW Teacher: René Hauck (N Lehr- und Forschungspersonal)
- ZHAW Teacher: Nicolas Duneau (N Extern)
- ZHAW Teacher: René Hauck (N Lehr- und Forschungspersonal)
- ZHAW Teacher: Ivo Kaelin (N Lehr- und Forschungspersonal)
- ZHAW Teacher: Corinne Lutz (N Angestellte)
- ZHAW Teacher: Thomas Ott (N Professor)
- ZHAW Teacher: Laura Peyer (N Angestellte)
- ZHAW Teacher: Céline Reinbold (N Lehr- und Forschungspersonal)
- ZHAW Teacher: Martin Rerabek (N Lehr- und Forschungspersonal)
- ZHAW Teacher: Quy Vo-Reinhard
- ZHAW Teacher: Samuel Wehrli (N Lehr- und Forschungspersonal)
Machine learning is a scientific discipline that allows a system to learn from data and act consequently without explicit programming.
Nowadays, machine learning forms an inevitable part of life and many of us uses it many times every day without noticing it.
This
course introduces basic principles of machine learning and creates a
fundamental intuition of its algorithms. Provided topics include: 1)
supervised learning (parametric and non-parametric algorithms,
regression, classification, support vector machines), 2) unsupervised
learning (dimensionality reduction, clustering).
The course will also focus on general machine learning good practices, model performance evaluation and validation.
To
introduce machine learning algorithms, a Python programming language
is used.
Basics of Python programming including data processing and analysis and data visualization is also part of this course.
- ZHAW Teacher: Martin Rerabek (N Lehr- und Forschungspersonal)
- ZHAW Teacher: Martin Schüle (N Lehr- und Forschungspersonal)
- ZHAW Teacher: René Hauck (N Lehr- und Forschungspersonal)