“What I cannot create, I do not understand.”
-- R. Feynman

Welcome to my teaching page! As an educator in data science, machine learning, and statistics, my philosophy is deeply rooted in a hands-on, creation-focused approach. I firmly believe that profound comprehension of the subject matter arises from application and construction. This philosophy has guided my course design and enabled me to cultivate an engaging, experiment-based learning environment. Here, students are actively challenged to build and experiment, equipping them with the essential skills needed for their future careers.

Current Teaching

Bachelor and Masterthesis at HTWG

• Information for potential Bachelor and Master Students link
• Lists of Supervised Students link

Courses at HTWG (2018-)

• Statistik (Bachelor) moodle
• BI (Bachelor) moodle
• Machine Learning (Master) moodle
• Deep Learning (Master) moodle
• Data Science (Master) moodle
• Data Analysis (Master) moodle

Continous Education

Together with Beate Sick I teach the following courses.

• Deep Learning (CAS Machine Intelligence, ZHAW Winterthur) from 2017- (yearly)
  • https://tensorchiefs.github.io/dl_course_2023/
• Deep Learning: A Probabilistic Approach (DAS and CAS in Applied Statistics, ETH Zurich) from 2020- (every 2nd year)
  • Course https://tensorchiefs.github.io/dl_rcourse_202/

Courses at ZHAW (2013-2018)

• Wahrscheinlichkeit und Statsistik (WaST, Bachelor)
  • WaST1 Descriptive Statistik 2 ECTS
  • WaST2 Wahrscheinlichkeitsrechung 4 ECTS
  • WaST3 Schliessende Statistik 6 ECTS
• Stochastische Prozesse (Bachelor)
• Statistical Datamining (Bachelor)
• Machine Learning (Master)

Bachelor, Project Thesis and Master Thesis supervised at the ZHAW link

Continous Education

Teaching done for the master of Advanced Studies (MAS Data Science) and certificate of Advanced Studies (CAS Data Science Applications)

• Deep Learning 8 4h lectures for CAS Machine Perception together with Beate Sick tensorchiefs.github.io/dl_course/, Spring 2017
• (Social-) Network Analysis 5 4h lectures for the CAS Statistical Modelling, Spring 2017
• Big Data Visualization in the CAS Data Science Applications Fall 2015
  • day 1 (ggplot) slides
  • day 2 (visualization of high-dimensional data) slides
  • day 3 (introduction to network analysis) slides
• Deep Learning in the CAS Fall 2016 slides github

Other

• Tutorials on deep learning, especially using TensorFlow github repository
• A tutorial session for teachers in data analysis based on our booklet: Datenanalyse; Anregungen zur Umsetzung der “Datenanalyse” im eidg. Rahmenlehrplan 2012 für die Berufsmaturität; Beate Sick, Oliver Dürr