Prerequisites

To participate in the summer school students need to master the basics in Linear Algebra and Analysis and have an advanced level in Python programming.

Preparatory material to reach a basic level in Machine Learning will be distributed beforehand. It is expected that the students prepare themselves with these documents for the summer school.

English Level B2 is compulsory.

Program structure

During the mornings, you will receive inputs and lectures, followed by structured exercises in a lab. In the afternoon you will work in teams on a challenge /task as project work. The project work will last for the entire duration of teh summer school, at ZHAW as well as at GVSU.

Topical and touristic excursion complement the academic curriculum.

Program

Download the detailed programme for weeks 1 & 2 at ZHAW School of Engineering

Week 1 at ZHAW: Data Engineering, lecturer: Dr. Jonathan Fürst

  Sun 29.6 Mon 30.7 Tue 1.7 Wen 2.7 Thu 3.7 Fri 4.7 Sat 5.7 Sun 6.7.
Morning Arrival US guests Lecture: Welcome & Intro in Data Science Process and Analysis Lecture: Data Aquisition & Storage Lecture: Data Preparation Lecture: Data Pipelines and Workflow Orchestration Excursion to Oracle Schweiz- Cloud Applications und Cloud Platform Excursion Rhine Fall & Alpstein Mountain Range incl overnight stay Short hike and transfer back to Winterthur
Afternoon Welcome garden party in Rikon, incl. dinner 4 pm Data Science Project: Introduction and selection of project Guest Talk Marcel Neidinger, Amazone Web Services and Data Science Project Data Science Project: Project work in groups and individual supervision Project Pitches 1-4 pm: Excursion to Kistler AG. 4pm: Frack parade and night of technology at ZHAW SoE Excursion Rhine Fall & Alpstein Mountain Range incl. overnight stay, Gatshaus Ebenalp Free time
Evening Dinner in Rikon, all participants Dinner US guests only 6 pm dinner all participants, incl. CH students, Restaurant Obergass Dinner US guests only Dinner US guests only Dinner all participants, incl. CH students at night of technology SoE (vouchers for food stands) Dinner at Gatshaus Ebenalp No arranged dinner

Week 2: Machine Learning (ZHAW), lecturers Dr. Manuel Dömer and Dr. Andreas Weiler

  Mon 7.7 Tue 8.7 Wen 9.7 Thu 10.7 Fri 11.7 Sat 12.7 Sun 13.7
Morning Lecture: Lecture in Machine Learning: Regression, Classification and Evaluation Lecture: Lecture Association Rules and Recommender Systems Data Science Project: Project work Data Science Project: Project work Full day excursion to Berne incl. visit of the federal parliament building Transfer to Allendale, Michigan, USA Transfer to Allendale, Michigan, USA
Afternoon Unsupervised Learning: Clustering, Anomaly Detection and Evaluation Data Science Project: Project work in groups and individual supervision Data Science Project: Project work in groups and individual supervision Data Science Project: Project presentations and discussions Full day excursion to Berne incl visit of the federal parliament building Transfer to Allendale, Michigan, USA Transfer to Allendale, Michigan, USA
Evening Dinner US guests only 6 pm dinner all participants, incl. CH students, Restaurant Obergass 6 pm dinner all participants, incl. CH students, Restaurant Sonne Dinner US guests only 5.30 pm farewell dinner for all participants, incl. CH students Restaurant Altes Tramdepot in Bern    

Week 3: Deep Learning (GVSU), lecturer Dr. Denton Bobeldyk

  Mon 14.7 Tue 15.7 Wen 16.7 Thu 17.7 Fri 18.7 Sat 19.7
Morning Lecture: Convolutional Neural Networks, Deep learning intro, Group Lab Lecture: Deep learning Digital Image Processing Group Lab Lecture: Deep Learning Dimensionality Reduction and Autoencoders Group Lab Lecture: LLMs and the future of AI Excursion to Meijer Gardens Tour Grand Haven Beach Excursion
Afternoon Lab/Final project work Lab/Final project work Lab/Final project work Lab/Final project work Gilmore Car Museum, dinner at Kalamazoo Blues Fest 2025 6 pm: Grand Rapids Soccer Club vs. Tulip City

Week 4: Information Visualization (GVSU), lecturer Prof. Ira Woodring

  Mon 21.7 Tue 22.7 Wen 23.7 Thu 224.7 Fri 25.7 Sat 26.7
Morning Lecture: Introduction to Data Vis including the libraries we will use Lecture: Design Principles and Best Practices Lecture: Design Principles and Best Practices Lecture: User-Centered Design Excursion to Chicago Free Time in Chicago and closure of summer school
Afternoon Information Visualization Lab/Final Project Work Information Visualization Lab/Final Project Work Information Visualization Lab/Final Project Work Information Visualization Lab/Final Project Work: Project presentations, evaluation, celebration Free time in Chicago and overnight stay at Chicago Hostel-International