AI/CO Education

This site is a comprehensive living document of all AI / Cognitive Science related educational offers in Switzerland. The list is opt-in and in the order of appearance.
Please report (your) missing or updated courses via email to SGAICO, providing the information listed below.

IRG – Information Retrieval Grundlagen B.Sc. (3rd year) Prof. Dr. Martin Braschler ZHAW School of Engineering Foundations of Text Analysis and Retrieval: Analysing unstructured data. more


DSSY – Decision Support Systems B.Sc. (3rd year) Dr. Kurt Stockinger,

Dr. Thilo Stadelmann

ZHAW School of Engineering Data Warehousing & Big Data: Analyzing structured data. more
Grundlagen der künstlichen Intelligenz B.Sc. (3rd year) Prof. Dr. Malte Helmert

University of Basel

Die Vorlesung bietet eine Einführung in die grundlegenden Sichtweisen, Probleme, Methoden und Techniken der Künstlichen Intelligenz.

Thematische Schwerpunkte: Einführung und historische Entwicklung der KI, der Agentenbegriff in der KI, Problemlösen und Suche, Logik und Repräsentation, Handlungsplanung, Darstellung und Verarbeitung unsicheren Wissens.

Search and optimization M.Sc. Prof. Dr. Malte Helmert University of Basel

The seminar focuses on informed state-space search (search algorithms, heuristics).


Maschinelles Lernen in der Sprachverarbeitung


Dr. Simon Clematide

University of Zurich

An introduction to machine learning approaches including SVM, Logistic Regression, Graphical Models.


Quantitative Methoden in der Computerlinguistik


Dr. Manfred Klenner

University of Zurich

An introduction to statistics and machine learning (distributions, hypothesis testing; regression, maximum entropy).

Aktuelle Fragestellungen der statistikbasierten Semantik Bachelor Dr. Manfred Klenner University of Zurich

Technics like Vector space and matrix factorization approaches (e.g. Latent Semantic Indexing) for the semantic modelling of natural languages.

XML Technologies and Semantic Web Bachelor

Dr. Fabio Rinaldi

University of Zurich Introduction to XML and the Semantic Web. more
Maschinelle Übersetzung und Parallele Korpora Master

Prof. Dr. Martin Volk

University of Zurich

Overview on techniques in the field of statistical and hybrid machine translation and parallel corpora (e.g. parallel treebanks).

Human Language Technology: Applications to Information Access Doctoral course (PhD) Dr. Andrei Popescu-Belis EPFL (EDEE and EDIC doctoral schools) This course introduces recent applications of human language technology, focusing on the problem of accessing text-based information across three main types of barriers: the quantity barrier, the crosslingual barrier, and the subjective barrier. more
Information Retrieval M.Sc. Prof. Dr. Stephane Marchand-Maillet

University of Geneva, Dept. of Computer Science

Fundemental aspects of Information Retrieval and associated Indexing. more
Information Analysis and Processing M.Sc. Prof. Dr. Stephane Marchand-Maillet University of Geneva, Dept. of Computer Science Fundemental aspects of Data Analysis and Information Theory. more

Artificial Intelligence

B.Sc. (3rd year) Prof. Dr. Marc Pouly,

Prof. Dr. Jana Koehler

Lucerne University of Applied Sciences and Arts (HSLU)

Basic techniques for designing and implementing intelligent agents structured according to knowledge representation, problem solving and machine learning. Topics include Constraint Programming, Planning and Scheduling, Game Theory, Bayesian networks and Markov chains. more
DAS in Data Science Prof. education Dr. Kurt Stockinger ZHAW School of Engineering Das Diploma of Advanced Studies (DAS) ist interdisziplinär aufgebaut und vermittelt Fähigkeiten etwa aus den Bereichen Data Warehousing & Big Data, Information Retrieval & Text Analytics sowie Statistics & Machine Learning. IT-Grundlagen, explorative Datenanalyse, Datenvisualisierung, Data Product Design und rechtlich-ethische Aspekte runden die Fähigkeiten als Daten-Allrounder ab. more
Einführung in die Multilinguale Textanalyse Master Prof. Dr. Martin Volk University of Zurich This course introduces the methods of automatic corpus annotation for both monolingual and multilingual corpora.


Techniken der Semantikanalyse Master Prof. Dr. Martin Volk University of Zurich This course introduces topics in the automatic semantic analysis of parallel corpora.


Master in Informatics / Intelligent Systems Master Prof. Dr. Jürgen Schmidhuber University of Lugano USI & Swiss AI Lab IDSIA A Master’s in Computer Science, with a Focus on Artificial Intelligence. Taught by award-winning experts of the Swiss AI Lab, IDSIA, and the Faculty of Informatics at the University of Lugano (USI). In the scenic southern part of Switzerland, the world’s leading science nation! more
AIC – Automatisation avancée, intelligence artificielle et cognitique B.Sc. (3rd year) and C.E. Prof. Dr. Jean-Daniel Dessimoz HESSO HEIG-VD Définitions, et nombreux exemples; manipulations de laboratoires et relatives. Exemple clip vidéo: more
Intelligence Artificielle B.Sc. (3rd year) Prof. Dr. Boi Faltings EPFL Introduction to AI (in French) using the book at more
Intelligent Agents Master Prof. Dr. Boi Faltings EPFL Theory and practice of agent and multi-agent systems: reactive and deliberative agents, multi-agent systems, computational game theory. Includes a mini-project programmed in Java. more
Learning and Intelligent Systems Bachelor Prof. Dr. Andreas Krause ETHZ The course introduces the foudations of learning and making predictions based on data. more
Data Mining: Learning from Large Data Sets Master Prof. Dr. Andreas Krause ETHZ Many scientific and commercial applications require insights from massive, high-dimensional data sets. This courses introduces principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course both covers theoretical foundations and practical applications. more
Probabilistic Artificial Intelligence Master Prof. Dr. Andreas Krause ETHZ This course introduces core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. more
Computational Statistics Master Dr. Martin Mächler,

Prof. Dr. Peter L. Bühlmann 

ETHZ “Computational Statistics” deals with modern methods of data analysis (aka “data science”) for prediction and inference. An overview of existing methodology is provided and also by the exercises, the student is taught to choose among possible models and about their algorithms and to validate them using graphical methods and simulation based approaches. more
Information Retrieval Master Prof. Dr. Thomas Hofmann ETHZ Introduction to information retrieval with a focus on text documents and images. Main topics comprise extraction of characteristic features from documents, index structures, retrieval models, search algorithms, benchmarking, and feedback mechanisms. Searching the web, images and XML collections demonstrate recent applications of information retrieval and their implementation. more
Big Data Master Prof. Dr. Thomas Hofmann ETHZ One of the key challenges of the information society is to turn data into information, information into knowledge, and knowledge into value. To turn data into value in this way involves collecting large volumes of data, possibly from many and diverse data sources, processing the data fast, and applying complex operations to the data. more
Probabilistic Graphical Models for Image Analysis Master Dr. Brian Victor McWilliams ETHZ This course will focus on the algorithms for inference and learning with statistical models. We use a framework called probabilistic graphical models which include Bayesian Networks and Markov Random Fields. We will use examples from traditional vision problems such as image registration and image segmentation, as well as recent problems such as object recognition. more
Imaging and Computer Vision Master Prof. Dr. Gábor Székely
et al.
ETHZ Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. more
Computer Vision Master Prof. Dr. Marc Pollefey,

Prof. Dr. Luc Van Gool

ETHZ The goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises. more
Statistical Learning Theory Master Prof. Dr. Joachim M. Buhmann ETHZ The course covers advanced methods of statistical learning : PAC learning and statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models. more
Computational Intelligence Lab Master Prof. Dr. Thomas Hofmann ETHZ This laboratory course teaches fundamental concepts in computational science and machine learning based on matrix factorization. This method provides a powerful framework of numerical linear algebra that encompasses many important techniques, such as dimension reduction, clustering, combinatorial optimization and sparse coding. more
Introduction to Natural Language Processing Master Dr. Enrique Alfonseca Cubero,

Dr. Massimiliano Ciaramita

ETHZ This course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems. more
Pattern classification and machine learning Dr. Mohammad Emtiyaz Khan EPFL Pattern classification occupies a central role in machine learning from data. In this course, basic principles and methods underlying machine learning will be introduced. The student will learn few basic methods, how they relate to each other, and why they work. more
Information Engineering 1 & 2 Bachelor 3rd year Prof. Dr. martin Braschler
et al.
ZHAW School of Engineering Information Engineering teaches foundational methods and processes to design and develop information systems. This includes creating, distributing and unlocking the information contained in structured and unstructured data. more
Economics and Computation BSc + MSc Prof. Dr. Sven Seuken University of Zurich In this course, we cover the interplay between economic thinking and computational thinking. Topics covered include: game theory, mechanism design, p2p file-sharing, eBay auctions, advertising auctions, combinatorial auctions, matching markets, and computational social choice. more

AI Winter School

BSc + MSc Prof. Dr. Thomas Koller,

Presentation of Project by Companies,

Group work on Project

Lucerne University of Applied Sciences and Arts (HSLU)

The Lucerne Winter School on Artificial Intelligence addresses local and international bachelor’s and master’s students interested in Artificial Intelligence and project work with a local company. The working language of the week is English. more
Cognitive Robotics Lab

B.Sc. (3rd year) Prof. Dr. Jana Koehler

Lucerne University of Applied Sciences and Arts (HSLU)

Introduction to robot programming; Basic Kinematics. Actuation of actuators; Processing sensor data; Programming an autonomous, mobile robot. Final competition. more

Machine Learning

B.Sc. (3rd year) Prof. Dr. Marc Pouly

Lucerne University of Applied Sciences and Arts (HSLU)

Basic techniques, tools and architectures of machine learning with application focus E-Commerce including regression analysis, classification, clustering, detection of anomalies and recommender systems. more

Smart Step Artificial Intelligence

Post-graduate education for professionals Prof. Dr. Jana Koehler,

Prof. Dr. Marc Pouly,

Prof. Dr. Thomas Koller

Lucerne University of Applied Sciences and Arts (HSLU)

The course provides important information about AI technologies to responsibles and decision-makers. It is discussed in which business cases the technologies can be used today. The participants will learn what technological innovation is expected in the near future and how these innovations will impact business models and processes in companies. more
CAS in Big Data and Machine Learning Post-graduate education for professionals Prof. Martin Volk,

Prof. Renato Pajarola

University of Zurich This multidisciplinary course gives an overview of the most important innovations in Deep Learning for text and image data more
Machine Learning BSc (3rd year) Andres Perez-Uribe HES-SO/HEIG-VD Introduction to Machine Learning approach and techniques. Contents including Feature extraction, Neural Networks, Unsupervised learning and Genetic Algorithms. t.b.d.
Machine Learning MSc Andres Perez-Uribe,

Jean Hennebert

HES-SO Introduction to Machine Learning approach and techniques. Contents including Linear regression, Bayes, SVM, Clustering, Neural Networks, Deep Learning, Recurrent Neural Networks, Dimensionality reduction, Autoencoders. t.b.d.
Machine Learning on Big Data MSc Andres Perez-Uribe,

Carlos Pena

HES-SO Feature engineering for image analysis, very large data sets, very high dimensional data, interpretability, Machine Learning applied to biological data and time-series, anomaly detection. t.b.d.