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 constantly updated.
Please report corrections, updates or new information to us via our email sgaico@swissinformatics.org.

B.Sc. – Bachelor Level
M.Sc. – Master Level
PhD   – Doktorate Level
MAS   – Postgraduate education for professionals
DAS   – Postgraduate education for professionals
CAS   – Postgraduate education for professionals
Smart Step – Postgraduate education for professionals
C.E. –

Basel (University)

Grundlagen der künstlichen Intelligenz  – Prof. Dr. Malte Helmert  Link
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.  (B.Sc.)

Search and optimization – Prof. Dr. Malte Helmert  Link
The seminar focuses on informed state-space search (search algorithms, heuristics). (M.Sc.)

Geneva (University)

Information Retrieval – Prof. Dr. Stephane Marchand-Maillet Link
Fundamental aspects of Information Retrieval and associated Indexing. (M.Sc.)

Information Analysis and Processing – Prof. Dr. Stephane Marchand-Maillet Link
Fundamental aspects of Data Analysis and Information Theory. (M.Sc.)

Lausanne (EPFL)

Intelligence Artificielle – Prof. Dr. Boi Faltings Link
Introduction to AI (in French) using the book at http://www.intelligence-artificielle.ch/ (B.Sc.)

Intelligent Agents – Prof. Dr. Boi Faltings Link
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. (M.Sc.)

Human Language Technology: Applications to Information Access – Dr. Andrei Popescu-Belis Link
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. (PhD, EDEE and EDIC doctoral schools)

Pattern classification and machine learning – Dr. Mohammad Emtiyaz Khan Link
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.

Lugano (University/Swiss AI Lab IDSIA)

Master in Informatics / Intelligent Systems – Prof. Dr. Jürgen Schmidhuber Link
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! (M.Sc.)

Romandie (Hesso – Haute école spécialisée de Suisse occidentale)

AIC : Automatisation avancée, intelligence artificielle et cognitique – Prof. Dr. Jean-Daniel Dessimoz Link
Définitions, et nombreux exemples; manipulations de laboratoires et relatives. Exemple clip vidéo: http://pfc-y.populus.org/rub/3   (B.Sc. and C.E.)

Machine Learning – Prof. Andres Perez-Uribe
Introduction to Machine Learning approach and techniques. Contents including Feature extraction, Neural Networks, Unsupervised learning and Genetic Algorithms. (B.Sc.)

Machine Learning – Prof. Andres Perez-Uribe, Prof. Jean Hennebert
Introduction to Machine Learning approach and techniques. Contents including Linear regression, Bayes, SVM, Clustering, Neural Networks, Deep Learning, Recurrent Neural Networks, Dimensionality reduction, Autoencoders. (M.Sc.)

Machine Learning on Big Data – Prof. Andres Perez-Uribe, Prof. Carlos Pena
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. (M.Sc.)

Rotkreuz (Hochschule Luzern)

Artificial Intelligence – Prof. Dr. Marc Pouly et al  Link
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. (B.Sc.)

AI Winter School – Prof. Dr. Thomas Koller et al. Link
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. (B.Sc./M.Sc.)

Cognitive Robotics Lab – Prof. Dr. Jana Koehler
Vermittelt die Grundlagen des Einsatzes und der Programmierung kognitiver Verhalten auf humanoiden Roboterplattformen. Am Beispiel des Softbanks Pepper Roboters entwickeln Sie intelligente Fähigkeiten, die einen Roboter in die Lage versetzen, mit Menschen zu interagieren und einfache Aufgaben zu übernehmen, die der Mensch an den Roboter überträgt. (B.Sc.)

Machine Learning – Prof. Dr. Marc Pouly
Basic techniques, tools and architectures of machine learning with application focus E-Commerce including regression analysis, classification, clustering, detection of anomalies and recommender systems. (B.Sc.) Link

Smart Step Artificial Intelligence – Prof. Dr. Jana Koehler et al. Link
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. (CAS Level Fachkurs)

Zurich (ETH)

Learning and Intelligent Systems – Prof. Dr. Andreas Krause  Link
The course introduces the foudations of learning and making predictions based on data. (B.Sc.)

Data Mining: Learning from Large Data Sets – Prof. Dr. Andreas Krause Link
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. (M.Sc.)

Probabilistic Artificial Intelligence – Prof. Dr. Andreas Krause Link
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. (M.Sc.)

Computational Statistics – Dr. Martin Mächler, Prof. Dr. Peter L. Bühlmann Link
“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. (M.Sc.)

Information Retrieval – Prof. Dr. Thomas Hofmann Link
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. (M.Sc.)

Big Data – Prof. Dr. Thomas Hofmann Link
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. (M.Sc.)

Probabilistic Graphical Models for Image Analysis – Dr. Brian Victor McWilliams Link
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. (M.Sc.)

Imaging and Computer Vision – Prof. Dr. Gábor Székely et al. more
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. (M.Sc.)

Computer Vision – Prof. Dr. Marc Pollefey, Prof. Dr. Luc Van Gool Link
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. (M.Sc.)

Statistical Learning Theory – Prof. Dr. Joachim M. Buhmann Link
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. (M.Sc.)

Computational Intelligence Lab – Prof. Dr. Thomas Hofmann Link
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. (M.Sc.)

Introduction to Natural Language Processing – Dr. Enrique Alfonseca Cubero, Dr. Massimiliano Ciaramita  Link
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. (M.Sc.)

Zurich (ZHAW – Hochschule der Angewandten Wissenschaften)

Künstliche Intelligenz 1 & 2 – Dr. Thilo Stadelmann, Dr. Mark Cieliebak Link
Diese stark praxisorientierte Einführung in ausgewählte Grundlagen der KI und des Maschinellen Lernens (ML) vermittelt konkrete hands-on Problemlösungskompetenz für alltägliche Informatik-Herausforderungen und richtet sich an alle, die von mitdenkender Software fasziniert sind. Sie eignet sich u.a. hervorragend für angehende Softwareingenieure, zukünftige Data Scientists sowie als Grundlage weiterer interdisziplinärer Vertiefungen etwa in Bereichen wie Information Engineering, Sprachverarbeitung, Computer Vision und Robotik.(B.Sc.)

Machine Learning – Dr. Thilo Stadelmann et al. Link
This course builds upon basic knowledge in math, programming and analytics/statistics as is typically gained in respective undergraduate courses of diverse engineering disciplines. From there, it teaches the foundations of modern machine learning techniques in a way that focuses on practical applicability to real-world problems. The complete process of building a learning system is considered: formulating the task at hand as a learning problem; extracting useful features from the available data; choosing and parameterizing a suitable learning algorithm. Covered topics include cross-cutting concerns like ML system design and debugging (how to get intuition into learned models and results) as well as feature engineering; covered algorithms include (amongst others) Support Vector Machines (SVM) and the emerging champion of ML methods, supervised and unsupervised deep learning techniques.(M.Sc.)

Data Science – Prof. Dr. Kurt Stockinger et al. Link
Die im MAS Data Science erworbenen Kenntnisse ermöglichen es, komplexe Fragestellungen an der Schnittstelle zwischen Daten, IT und Business zu beantworten, neue Lösungswege aufzuzeigen und sie alleine oder im Team zu erarbeiten. Das Angebot ist modular aufgebaut und besteht aus insgesamt fünf Certificates of Advanced Studies (CAS). Die Absolventen erwerben sowohl theoretische Grundlagen als auch praktische Fähigkeiten in den folgenden Bereichen:Data Warehousing & Big Data, Information Retrieval & Text Analytics, Statistik & Machine Learning / Deep Learning, Design & Entwicklung von Data Products, Datenschutz & Datensicherheit. (MAS, DAS, CAS)

Information Engineering 1 & 2 – Prof. Dr. Martin Braschler, Prof. Dr. Kurt Stockinger Link
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. Analyzing structured data: Data Warehousing & Big Data. Analysis of unstructured data: Foundations of Text Analysis and Retrieval. (B.Sc.)

Ph.D. Network in Data Science – Prof. Dr. Dirk Wilhelm et al. Link
ZHAW and SUPSI set up a joint PhD network with University of Zurich (UZH) and University of Neuchâtel (UNINE). Graduates from UAS master programs as well as from university master programs have access to this PhD network. The PhD should be open for students holding a master degree in the fields of Science, Technology, Engineering and Mathematics (STEM), business management and economy, or other fields constituting Data Science. Industry companies organized in the Swiss Alliance for Data-Intensive Services will have the possibility to fund students in the PhD network. (Ph.D.)

Master of Science in Engineering, sub-discipline Data Science – Link
The joint master programme in engineering of all Swiss UAS’ offers a major in Data Science within ICT, with new modules on deep learning, computer vision, time series analysis and text analysis as well as advanced statistical modelling. (M.Sc.)

Zurich (University)

Maschinelles Lernen in der Sprachverarbeitung – Dr. Simon Clematide Link
An introduction to machine learning approaches including SVM, Logistic Regression, Graphical Models. (M.Sc.)

Quantitative Methoden in der Computerlinguistik – Dr. Manfred Klenner Link
An introduction to statistics and machine learning (distributions, hypothesis testing; regression, maximum entropy). (B.Sc.)

Aktuelle Fragestellungen der statistikbasierten Semantik  – Dr. Manfred Klenner Link
Technics like Vector space and matrix factorization approaches (e.g. Latent Semantic Indexing) for the semantic modelling of natural languages. (B.Sc.)

Einführung in die Multilinguale Textanalyse  – Prof. Dr. Martin Volk  Link
Overview on techniques in the field of statistical and hybrid machine translation and parallel corpora (e.g. parallel treebanks). (M.Sc.)

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

XML Technologies and Semantic Web – Dr. Fabio Rinaldi Link
Introduction to XML and the Semantic Web. (B.Sc.)

Maschinelle Übersetzung und Parallele Korpora – Prof. Dr. Martin Volk Link
This course introduces the methods of automatic corpus annotation for both monolingual and multilingual corpora. (M.Sc.)

Economics and Computation  – Prof. Dr. Sven Seuken
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. (B.Sc./ M.Sc.)

CAS in Big Data and Machine Learning – Prof. Martin Volk, Prof. Renato Pajarola Link
This multidisciplinary course gives an overview of the most important innovations in Deep Learning for text and image data  (CAS)

CAS in Big Data and Machine Learning – Prof. Martin Volk, Prof. Renato Pajarola Link
This multidisciplinary course gives an overview of the most important innovations in Deep Learning for text and image data.