Swiss AI Education Table

UniversityEducationContact Info
Universität BaselGrundlagen der Künstlichen Intelligenz : 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.)
Prof. Dr. Malte Helmert
+ 41 61 207 05 48
Departement of Mathematics and Computer Science, Spiegelgasse 1 CH - 4051 Basel, Switzerland
Universität BaselSearch and optimization : The seminar focuses on informed state-space search (search algorithms, heuristics). (M.Sc.)Prof. Dr. Malte Helmert
+ 41 61 207 05 48
Departement of Mathematics and Computer Science, Spiegelgasse 1 CH - 4051 Basel, Switzerland
Université de GenèveInformation Retrieval : Fundamental aspects of Information Retrieval and associated Indexing. (M.Sc.)Prof. Dr. Stephane Marchand-Maillet
+ 41 22 379 01 54
Battelle Bâtiment A, Route de Drize 7, 1227 Carouge
Université de GenèveInformation Analysis and Processing : Fundamental aspects of Data Analysis and Information Theory. (M.Sc.)Prof. Dr. Stephane Marchand-Maillet
+ 41 22 379 01 54
Battelle Bâtiment A, Route de Drize 7, 1227 Carouge
EPFLIntelligence Artificielle : Introduction to AI (in French) using the book at (B.Sc.)Prof. Dr. Boi Faltings
+ 41 21 69 32735
EPFL IC IINFCOM LIA, INR 230 (Bâtiment INR), Station 14, CH-1015 Lausanne
EPFLIntelligent Agents : 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.)
Prof. Dr. Boi Faltings
+ 41 21 69 32735
EPFL IC IINFCOM LIA, INR 230 (Bâtiment INR), Station 14, CH-1015 Lausanne
EPFL"Human Language technology : Applications to Information Access" : 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)
Dr. Andrei Popescu-Belis
EPFLPattern classification and machine learning : 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.
Dr. Mohammad Emtiyaz Khan
Lugano University/Swiss AI Lab ID SIAMaster in Informatics / Intelligent Systems : 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.)
Prof. Dr. Jürgen Schmidhuber
+ 41 58 666 66 62
HES-SO Haute école spécialisée de Suisse occidentale"AIC : Automatisation avancée, intelligence artificielle et cognitique" : Définitions, et nombreux exemples;
manipulations de laboratoires et relatives. Exemple clip vidéo: (B.Sc. and C.E.)
Prof. Dr. Jean-Daniel Dessimoz
HES-SO Haute école spécialisée de Suisse occidentaleMachine Learning : Introduction to Machine Learning approach and techniques.
Contents including Feature extraction, Neural Networks, Unsupervised learning and Genetic Algorithms. (B.Sc.)
Prof. Andres Perez-Uribe
HES-SO Haute école spécialisée de Suisse occidentaleMachine Learning : 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.)
Prof. Andres Perez-Uribe, Prof. Jean Hennebert
HES-SO Haute école spécialisée de Suisse occidentaleMachine Learning on Big Data : 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.)
Prof. Andres Perez-Uribe, Prof. Carlos Pena
Hochschule LuzernCAS Artificial Intelligence : Künstliche Intelligenz verstehen, innovativ anwenden und hinter den Hype blicken –
Der Kurs bietet einen fundierten Einstieg in die Algorithmen der Künstlichen Intelligenz (KI)
und vermittelt grundlegendes Wissen zu allen wichtigen Teilgebieten der KI.
Aktuelle Beispiele aus der Praxis machen KI erlebbar und befähigen die Teilnehmenden,
ihr erworbenes Wissen bei der Entwicklung KI-basierter Innovationen anzuwenden.
Prof. Dr. Jana Koehler et al
Hochschule LuzernArtificial Intelligence : 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.)
Prof. Dr. Marc Pouly et al.
Hochschule LuzernAI Winter School : 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.)
Prof. Dr. Thomas Koller et al.
Hochschule LuzernCognitive Robotics Lab : 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.)
Prof. Dr. Jana Koehler
Hochschule LuzernMachine Learning : 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.)
Prof. Dr. Marc Pouly
Hochschule LuzernSmart Step Artificial Intelligence : 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)
Prof. Dr. Jana Koehler et al
Hochschule LuzernBachelor of Science in Artificial Intelligence & Machine LearningDr. Donnacha Daly
076 553 50 93
Suurstoffi 1, 6343 Rotkreuz
ETH ZürichLearning and Intelligent Systems : The course introduces the foudations of learning and making predictions based on data. (B.Sc.)Prof. Dr. Andreas Krause
ETH Zürich"Data Mining : Learning from Large Data Sets" : 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.)
Prof. Dr. Andreas Krause
ETH ZürichProbabilistic Artificial Intelligence : 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.)
Prof. Dr. Andreas Krause
ETH ZürichComputational Statistics : “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.)
Dr. Martin Mächler, Prof. Dr. Peter L. Bühlmann
ETH ZürichInformation Retrieval : 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.)
Prof. Dr. Thomas Hofmann
Dep. Informatik, ETH Zürich, CAB F 48.1, Universitätstrasse 6, 8092 Zürich
ETH ZürichBig Data : 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.)
Prof. Dr. Thomas Hofmann
Dep. Informatik, ETH Zürich, CAB F 48.1, Universitätstrasse 6, 8092 Zürich
ETH ZürichProbabilistic Graphical Models for Image Analysis : 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.)
Dr. Brian Victor McWiliams
ETH Zürich, STD H 2, Stampfenbachstrasse 48, 8092 Zürich
ETH ZürichImaging and Computer Vison : 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.)
Prof. De. Gäbor Székely et al.
Institut für Bildverarbeitung, ETH Zürich, ETF C 117, Sternwartstrasse 7, 8092 Zürich
ETH ZürichComputer Vision : 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.)
Prof. Dr. Marc Pollefey, Prof. Dr. Luc Van Gool
Institut für Visual Computing, ETH Zürich, CNB G 105, Universitätstrasse 6, 8092 Zürich
ETH ZürichStatical Learning Theroy : 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.)
Prof. Dr. Joachim M. Buhmann
Institut für Maschinelles Lernen, ETH Zürich, CAB G 69.2, Universitätstrasse 6, 8092 Zürich
ETH ZürichComputational Intelligence Lab : 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.)
Prof. Dr. Thomas Hofmann
Dep. Informatik, ETH Zürich, CAB F 48.1, Universitätstrasse 6, 8092 Zürich
ETH ZürichIntroduction to Natural Language Processing : 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.)
Dr. Enrique Alfonseca Cubero, Dr. Massimiliano Ciaramita
Einsiedlerstrasse 71e, 8810 Horgen
ZHAW - Hochschule der Angewandten WissenschaftenKünstliche Intelligenz 1 & 2 : 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.)
Dr. Thilo Stadelmann, Dr. Mark Cieliebak
ZHAW - Hochschule der Angewandten WissenschaftenMachine Learning : 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.)
Dr. Thilo Stadelmann et al
ZHAW - Hochschule der Angewandten WissenschaftenData Science : 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)
De. Kurt Stockinger et al.
ZHAW - Hochschule der Angewandten WissenschaftenInformation Engineering 1 & 2 : 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.)
Prof. Dr. Martin Braschler, Prof. Dr. Kurt Stockinger
+ 41 58 934 69 52
ZHAW - Hochschule der Angewandten WissenschaftenPh.D. Network in Data Science : 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.)
Prof. Dr. Dirk Wilhelm et al.
ZHAW - Hochschule der Angewandten WissenschaftenMaster of Science in Engineering, sub-discipline Data Science : 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.)
Universität ZürichMaschinelles Lernen in der Sprachverarbeitung : An introduction to machine learning approaches including SVM,
Logistic Regression, Graphical Models. (M.Sc.)
Dr. Simon Clematide
Universität ZürichQuantitative Methoden in der Computerlinguistik : An introduction to statistics and machine learning
(distributions, hypothesis testing; regression, maximum entropy). (B.Sc.)
Dr. Manfred Klenner
Universität ZürichAktuelle Fragestellungen der statistikbasierten Semantik : Technics like Vector space and matrix factorization approaches
(e.g. Latent Semantic Indexing) for the semantic modelling of natural languages. (B.Sc.)
Dr. Manfred Klenner
Universität ZürichEinführung in die Multilinguale Textanalyse : Overview on techniques in the field of statistical
and hybrid machine translation and parallel corpora (e.g. parallel treebanks). (M.Sc.)
Prof. Dr. Martin Volk
Universität ZürichTechniken der Semantikanalyse : This course introduces topics in
the automatic semantic analysis of parallel corpora.
Prof. Dr. Martin Volk
Universität ZürichXML Technologies and Semantic Web : Introduction to XML and the Semantic Web. (B.Sc.)Dr. Fabio Rinaldi
Universität ZürichMaschinelle Übersetzung und Parallele Korpora : This course introduces the methods of
automatic corpus annotation for both monolingual and multilingual corpora. (M.Sc.)
Prof. Dr. Martin Volk
Universität ZürichEconomics and Computation : 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.)
Prof. Dr. Sven Seuken
Universität ZürichCAS in Big Data and Machine Learning : This multidisciplinary course gives an overview of the most
important innovations in Deep Learning for text and image data (CAS)
Prof. Martin Volk, Prof. Renato Pajarola
Universität ZürichCAS in Big Data and machine Learning : This multidisciplinary course gives an overview
of the most important innovations in Deep Learning for text and image data.
Prof. Martin Volk, Prof. Renato Pajarola