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|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.|
|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
|Master||University of Zurich|| |
An introduction to machine learning approaches including SVM, Logistic Regression, Graphical Models.
Quantitative Methoden in der Computerlinguistik
|Bachelor||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||University of Zurich||Introduction to XML and the Semantic Web.||more|
|Maschinelle Übersetzung und Parallele Korpora||Master||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.|
|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.|
Introduction to Artificial Intelligence
|B.Sc. (3rd year)|| |
Lucerne University of Applied Sciences and Arts (HSLU)
Grundlegende Techniken zum Design und Implementation von intelligenten Agenten strukturiert nach Wissensrepräsentation, Problemlösung und Maschinelles Lernen.
Themen sind u.a. Constraint Programmierung, Probabilistisches Schliessen, Planung und Scheduling, Regressionsanalyse, Neuronale Netze und Entscheidungsbäume.
|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.|
|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!|
|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: http://pfc-y.populus.org/rub/3||more|
|Intelligence Artificielle||B.Sc. (3rd year)||Prof. Dr. Boi Faltings||EPFL||Introduction to AI (in French) using the book at http://www.intelligence-artificielle.ch/||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 |
|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|
|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|