Machine Learning UZH

It's Never Too Late to Learn a New Skill! Learn to Code and Join Our 45+ Million Users. Enjoy Extra Quizzes & Projects and Exclusive Content. Practice with Our App. Enroll Today Learn Machine Learning Online At Your Own Pace. Start Today and Become an Expert in Days. Join Millions of Learners From Around The World Already Learning On Udemy UZH - Artificial Intelligence and Machine Learning Group - AIML Teaching. Department of Informatics. People. Open positions. AIML Teaching. Seminar Artificial Intelligence and Machine Learning. Lecture Deep Learning The learning objectives of this course are as follows: Get familiar with the concept of machine learning. Understand the basic theory behind various machine learning techniques. Apply different machine learning techniques and interpret the results. Instructors: Dr. Markus Meierer Patrick Bachmann. Contact: market-research@business.uzh.ch. Type: Lectur The Artificial Intelligence and Machine Learning group has just recently (July 2020) been created and currently contains only one member, Prof. Manuel Günther. The Background Modern machine learning algorithms, i.e., deep neural networks have changed the way, most researcher approach problems nowadays

The new e-book Machine Learning kompakt - avaible in German only - provides an introduction to Machine Learning and can be downloaded free of charge by UZH members at Springer Link. Co-authored by theoretical physicist Titus Neupert this e-book addresses applications of machine learning in various fields of science Artificial Intelligence and Machine Learning Group Department of Informatics University of Zurich Andreasstr. 15 / Office AND 2.54 8050 Zürich Switzerland. Phone: +41 44 635 71 40. E-Mail: guenther@ifi.uzh.c Machine Learning for Text Technologies. The Department of Computational Linguistics conducts research on machine learning for text technologies Courses with the necessary background include Economics and Computation and Introduction to Market Design at UZH, as well as Algorithmic Game Theory at ETH. To obtain the prior knowledge for machine learning, any introductory course on machine learning is sufficient

UZH - DDIS - Matthias Baumgartner

University of Zurich Department of Informatics Artificial Intelligence and Machine Learning Group Open position Machine learning techniques have been used for a long time [19], [20] to map visual inputs to actions. When the goal is obstacle avoidance, several works [21], [22], [23] obtained good results with simple biologically-inspired con-trollers based on optical ow features. More recently, deep learning techniques have also been adopted by Sermanet, Had Deep Learning Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. In our research, we apply deep learning to solve different mobile robot navigation problems, such as depth estimation, end-to-end navigation, and classification An introduction to machine learning. The final project have been handed out to the groups via email on 16.12.2019. If you are a student taking the exam and you (or your group leader) have not received this email, please contact us as soon as possible at the email address gabriele.visentin@math.uzh.ch

Machine Learning Basics - Kick-Start Your Career Toda

Machine Learning For Dummies - Python & R In Data Scienc

The University of Zurich has several internationally recognized research groups dedicated to data science, machine learning, remote sensing, biodiversity, and climate change. We also collaborate with several other institutions and companies in the fields of computer vision, machine learning and earth observation, in Switzerland and abroad Certificate of Advanced Studies UZH in Data Science and Machine Learning (10 ECTS Credits) Zielpublikum Informatikerinnen und Informatiker im Berufsumfeld mit Freude an «Life-long Learning», deren Abschluss einige Jahre zurückliegt und die sich mit den aktuellsten Forschungsergebnissen und Methoden einen Wissensvorsprung verschaffen wollen für die Berufspraxis der Zukunft Machine Learning (ML) is developing under the great promise that marketing can now be both more efficient and human. Cognitive systems, embedded or not into marketing software, are powering every single functional area of marketing and each step of the consumer journey The University of Zurich has several internationally recognized research groups dedicated to data science, machine learning and remote sensing, and we also collaborate with several other institutions and companies in the fields of computer vision, machine learning and earth observation, in Switzerland and abroad This independent research report describes the goal, process, and benefit of AI-driven marketing. It explores how marketing leverages machine learning models to automate, optimize, and augment the transformational process of data into actions and interactions with the scope of predicting behaviors, anticipating needs, and hyper-personalizing messages

UZH - Artificial Intelligence and Machine Learning Group

Machine learning algorithms are data analysis methods which search data sets for patterns and characteristic structures. Typical tasks are the classification of data, automatic regression and unsupervised model fitting. Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of. 1 Postdoc in Machine Learning. 100 %. With the launch of a new National Center of Competence in Research (NCCR) Evolving Language, which involves nearly 40 different research groups from a large variety of disciplines across Switzerland, we seek to hire a four-year postdoc position specialized in data science and in machine learning 2 Positions Specialized in Machine Learning 100 % With the launch of a new National Center of Competence in Research (NCCR) Evolving Language , which involves nearly 40 different research groups from a large variety of disciplines across Switzerland, we seek to fill two permanent positions as data science consultants specialized in machine learning at the University of Zurich The successful candidate will join the ERC project on Machine Learning-based Market Design (MIAMI) led by Prof. Dr. Sven Seuken. On a high level, the research question to be investigated in this project is how we can combine techniques from machine learning with market design to develop better market mechanisms. Your profil Schaffhauserstrasse 228, 8057 Zürich (www.zwb.uzh.ch) Aufbau Der CAS ist in zwei Module aufgeteilt: - «Data Science und Grundlagen des Machine Learning» - «Data Science und Anwendungen des Machine Learning» Um den CAS erfolgreich zu absolvieren, sind total 10 ECTS Credits nötig. Jedes Modul ergibt 5 ECTS Credits

Machine Learning - A non-technical introduction - UZ

In this study, various machine learning models were built to evaluate the quality of online health information using the DISCERN criteria, which were developed at University of Oxford. The results suggest that automating the quality assessment of online health information is feasible, representing an important step towards enabling patients to become informed partners in the health process Maskininlärning (engelska: machine learning) är ett område inom artificiell intelligens, och därmed inom datavetenskapen. Det handlar om metoder för att med data träna datorer att upptäcka och lära sig regler för att lösa en uppgift, utan att datorerna har programmerats med regler för just den uppgiften Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading cause of antibiotic resistance. We combine administrative and microbiological laboratory data from Denmark to train a machine learning algorithm predicting bacterial causes of urinary tract infections Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises. 30+ exercises. 25 lessons. 15 hours. Lectures from Google researchers. Real-world case studies. Interactive visualizations of algorithms in action Tired of clicking buttons? We thought so. We shape future workspaces with new technology! We are leveraging the benefits of RPA, Robotics, AI & Chatbots - AKO

Classes in the major study program in AI will teach you the foundations and advanced skills of artificial intelligence, such as deep learning, machine learning, computer graphics, computer vision for robotics, natural language processing, machine translation, coordination of complex systems, big-data analytics, combinatorial and approximation algorithms, randomized and online algorithms. Scientists at UZH and ETH Zurich have used machine learning methods to improve optoacoustic imaging. This relatively young medical imaging technique can be used for applications such as visualizing blood vessels, studying brain activity, characterizing skin lesions and diagnosing breast cancer The program has a core that focuses on general principles (Methodological Foundations, Numerical Methods for Differential Equations in Simulations, Advanced High-performance Computing, Methods for Visualizing Simulation Data, Machine Learning in Science). The core courses all have a strong practical component in code development and programming In this course, students will learn the basic tool kit of a data scientist, such as regression and classification algorithms, Bayesian statistics, and machine learning. Students will also have the opportunity to apply their knowledge on a 6 weeks long project on a data science topic of their choice Assistant Professorship in AI and Machine Learning (Non-tenure Track) starting in 2019. Candidates should hold a Ph.D. degree in Computer Science with specialization in Machine Learning and/or Data Mining including Deep Learning, lnteractive/ Cooperative Learning, statistical learning or privacy preserving modeling, and have an excellent record of academic achievements in the relevant fields

UZH - URPP Language and Space - Prof

Advanced Statistics and Machine Learning; Data Visualization; Ethics; Career prospects and further degree programs. With a Master's degree in Data Science, you will be one of the few informatics specialists in Switzerland who specialize particularly in analyzing and processing data - a field with a huge amount of potential for the. Applied Machine Learning Days (AMLD) 2020, 'AI & Cities' track on January 25-29, 2020 in Lausanne, Switzerland. ML4SEP 2019: The Workshop on Applied Machine Learning for Social & Environmental Problems on UZH Irchel on October 3-4, 2019 Prof. Dr. Sven Seuken (Department of Computer Science, UZH) Research Interests: Market Design, Artificial Intelligence, Economics and Computation, Computational Mechanism Design, Algorithmic Game Theory, Machine Learning E-Mail: seuken@ifi.uzh.ch Websit Epidemiology, Biostatistics, Machine Learning for Health Care, Internal Medicine. Matteo Berchier Research Assistant. Data Science, Human Disease Monitoring, IoT & Smart Sensors. Todor Gitchev Senior Research Assistant. Bioinformatics, Applied Mathematics, Genomics & Epigenetics, Algorithms

The Specialized Master in Computational Science has a core that focuses on general principles (Methodological Foundations, Numerical Methods for Differential Equations in Simulations, Advanced High-performance Computing, Methods for Visualizing Simulation Data, Machine Learning in Science). The core courses all have a strong practical component in code development and programming 2 Feb 2021 Machine Learning kompakt: New e-book gives a compact introduction into machine learning. The new e-book Machine Learning kompakt - avaible in German only - provides an introduction to Machine Learning and can be downloaded free of charge by UZH members at Springer Link

UZH - Department of Informatic

  1. Applied Machine Learning in Diagnostic Imaging Digital transformation based on data-driven technology has the potential to revolutionize medicine. Academia can play a major role in the develop-ment, validation and marketability of innovative solutions responding to the compelling needs of patients and healthcare professionals
  2. As starting point we characterize learning induced changes of neuronal ensemble activity in mice using in vivo population Ca2+-imaging, high-throughput image processing and data analysis methods that are commonly used in machine learning
  3. We developed a machine learning model of lymphatic tumor progression in head & neck cancer using the methodology of Bayesian networks and Hidden markov models, which is illustrated in figure 2. In this model, each lymph node level is associated with a binary random variable for the microscopic state, which indicates whether or not the level truly harbors tumor including occult metastases
  4. Applications of Machine Learning in High-Frequency Financial Time Series Prediction Rino R. Beeli February 18, 2021 Abstract Interest in the use of machine learning methods continues unabated, notably in empiri-cal nance and nancial time series prediction. The recent advent of powerful machine
  5. Educational specialist with experience in AI, machine learning, big data in plant sciences (20%, Post-doctoral position) The PSC is a successful center of excellence in plant sciences at the Universities of Zurich and Basel, as well as ETH Zurich. For 20 years, we have coordinated and developed high-quality teachin

UZH - Faculty of Science - Machine Learning kompakt: New e

The aim of the project is to develop a neuroprosthesis with thousands of electrodes driven by adaptive machine learning algorithms for a new brain-computer interfacing technology. We want to create a novel neuroprosthesis system that is lightweight, robust and portable, and which will remain effective for decades, explains Shih-Chii Liu http://www.w3.org/TR/owl-ref/ http://www.ksl.stanford.edu/people/dlm/webont/OWLFeatureSynopsisJune23.htm http://attempto.ifi.uzh.ch/site/docs/writing_owl_in_ace.htm In this context of neuroplasticity research, we are designing and implementing novel multi-modal paradigms (e.g. combined EEG eye-tracking), extracting and associate them with state of the art neuroscientific methods, such as functional network models, machine learning, longitudinal analyses and computational modeling Keywords: algorithm | genealogy | machine learning | microtargeting | probability | theology. Le foucaldien has just published a new research article as part of the collections Critical Genealogies and Algorithmic Governmentality written by Virgil W. Brower and entitled Genealogy of Algorithms: Datafication as Transvaluation.

UZH - Institut für Computerlinguistik - Dr

Based on machine learning, the business develops automated image evaluation programs for use in production machines; UZH spin-offs are companies founded by University of Zurich researchers who use their inventions to develop new technologies, products and services for the market BIO397 Applied machine learning [BC], VVZ, Online Course (block course) BIO409 Veterinary Medicine: comparative morphology and pathophysiology [BC], VVZ, Online Course (block course) BIO412 Introductory Course in laboratory Animal Science [EG], VVZ, Online Course. BIO416 Microscopy [C], VVZ, Online Course (online only via zoom, exam on site 15. Map of classical machine learning methods Valentina Boeva, Computer Science Dept., Institute for Machine Learning 26.10.2020 6 Machine learning Supervised Unsupervised learns on data with labels learns on data without labels Understand data structure Visualize data in 2D/3D Detect hidden features Automatize decisions • Primary diagnosi Session A: Causal Machine Learning [Zoom] . Anthony Strittmatter: Optimal Targeting in Fundraising: A Machine Learning Approach Martin Huber: Causal mediation analysis with double machine learning

Phone: +41 44 635 74 10. Telfax: +41 44 635 74 19. Address: Binzmuehlestrasse 14 / Box 24, CH-8050 Zurich, Switzerland. Room number: BIN 4.B.11. m.martin@psychologie. Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to quantify and analyze brain vasculature, named Vessel Machine learning and high-dimensional cytometry. For their study the researchers used high-dimensional cytometry to characterize the immune cells. This technology makes it possible to analyze millions of cells in hundreds of patients and determine their immune properties - in other words, their fingerprints

The Max Planck Society and the University of Zurich invite applications for interdisciplinary predoctoral fellowships in the field of Digital Visual Studies for a duration of 1+2 years.The program is part of a 5-year cross-institutional Digital Humanities research project funded by the MPG, supported by the Bibliotheca Hertziana - Max Planck Institute for Art History and collaborating with. kurssekretariat@id.uzh.ch. R: Web Scraping. Collecting and preprocessing data is always the first step in a data analysis project or in a machine learning pipeline. The web plays a crucial role here: Often, authoritative statistical data are published as tables on regularly updated websites

From von Neumann to Machine Learning: Equilibrium Computation and the Foundations of Deep Learning (John von Neumann Lecture) Links to Other Regular Seminar Series Microeconomic Seminar (ETH/UZH This workshop will bring together researchers coming from different backgrounds (computer vision, machine learning, and robotics) and applications, to discuss existing solutions, research problems, and the way forward to make robots interact with a permanently moving world To do this, an existing Python module called Pattern can be used for downloading tweets and providing various machine learning techniques. Aim and purpose Automatic analysis of whether a tweet is positive, negative or neutral with German as a target language

Machine Learning for Text Technologies - UZ

Lecture: Market Design and Machine Learning (Spring - UZ

Robotics and Perception Grou

Institute for Neuropsychology, UZH, and Institute for Computational Linguistics, UZH . Organised by: Maria Kliesch, Julia Bauer and Chantal Oderbolz. Website «Legal Gender Studies - Entwicklungen in der Schweiz «UZH Machine Learning Workshop. I'm going to live tweet @m_sendhil's talk on Economic Applications of Machine Learning at @econ_uzh today . We'll see 3 applications today. There have been hugely impressive engineering advances in this area in recent years. Sendhil starts by introducing concepts with the example of face recognition Open position: Educational specialist with experience in AI, machine learning, big data in plant sciences (20%, Scientific Assistant) The Zurich-Basel Plant Science Center is about to enrich the curriculum of its PhD Program in Plant Sciences to strengthen skills and knowledge in the fields of machine learning, artificial intelligence and big data in plant sciences Our approach to enzyme engineering consists of four different directions: traditional directed evolution techniques, machine-learning-guided directed evolution, unnatural amino acid incorporation, and method development for new approaches to enzyme engineering

Restoring Vision Through Electrical Stimulation. In a project under Horizon 2020, researchers from seven European organizations will examine how the vision of visually impaired people can be restored using electrical stimulation of the brain. The project is being coordinated by the University of Zurich and supported by the European Union with. In this proof of concept study, we exemplify such a machine learning approach for raw HRMS‐DIA data files. We evaluated a machine learning model using training, validation, and test sets of solvent and whole blood samples containing drugs (of abuse) common in forensic toxicology. For that purpose, different platforms were used Areas of expertise: collaborative design, innovative product development, manufacturing systems, concurrent engineering, knowledge-based expert system, AI-based machine learning technologies. Course: Innovation Management. Prof. Dr. Li, Wei. Professor of Economics, PhD, University of Michigan, Associate Dean for MBA, Cheung Kong Business Schoo

Nicolas Langer, Prof. Dr. Head of Discipline. Phone: +41 44 635 34 14. Room number: AND.4.56. n.langer@psychologie.uzh.ch. Our group is focusing on human brain & behavioral plasticity and the development of new research methods Using state of the art machine learning techniques, the team subsequently compared the brain scans between musicians, musicians with absolute pitch, and non-musicians — finding similar brain. Machine learning and data science in bioinformatics; Open science and reproducible research; I'm open minded and happy to explore new things. Feel free to contact me if you see poential collaboration opportunities : The type of machine learning they're looking to perform; In other words, it depends. However, R is the leading choice among data professionals who want to understand data, using statistical methods and graphs. It has several machine learning packages and advanced implementations for the top machine learning algorithms. R is also open source 2h+1h: 4.0: Prof. Dr. Patrick Cheridito : Thu. 10.00-12.00 (L) Thu. 12.00-13.00 (E) ETH II. Elective Courses Possible courses are given in the following table, further optional courses were offered in the Fall Semester; other courses may be taken upon a written request to the Steering Committee

UZH - Institute of Mathematics - Vorlesungen/Detail

  1. Advanced machine learning technologies have revolutionized image analysis in the recent years. In various vision tasks, current methods that utilize multi-layered neural networks, a.k.a. deep learning, reach near-human performance
  2. on Machine Learning for Autonomous Vehicles. Sydney, Australia, 2017. See DDD17 dataset. DDD20: DAVIS Driving Dataset 2020 : An additional 41h of DAVIS E2E driving data has been collected and organized. It includes mountain, highway, freeway, freeway, day and night driving including difficult glare conditions. See DDD20 website. PRED18 (was PRED16
  3. Machine learning for dynamic incentive problems Philipp Renner Department of Economics University of Lancaster p.renner@lancaster.ac.uk Simon Scheidegger Department of Banking and Finance University of Zurich simon.scheidegger@uzh.ch This version: January 17, 2018 Current version: this link Abstrac
  4. Tel.: +41 44 635 72 27. Fax.: +41 44 635 74 19. Anschrift: Binzmühlestrasse 14, Box 24, CH-8050 Zürich. Raumbezeichnung: BIN 4.B.22. m.luo@psychologie.uzh.c
  5. Fretica Fretica is a user-extendable toolbox for analyzing single-molecule fluorescence data. It is a Wolfram Mathematica package with a backend written in C++, which has been actively developed since 2010 by D. Nettels, B. Schuler, and co-workers. From within Mathematica Notebooks, raw data can be accessed and analyzed with almost three hundred highly optimized commands [

UZH - - Bjoern Menz

UZH - Science Communication - Publications

GitHub - matteocourthoud/Machine-Learning-for-Economic

  1. Open doctoral position for machine learning audio and hardware We have open positions for a doctoral student to develop new data-driven deep neural networks for audio tasks and sensor fusion for low-latency, high energy efficient edge devices and another to develop FPGA /ASIC implementations of data-driven DNNs
  2. ar Karl Schmid-Strasse 4 CH-8006 Zürich Tel. +41 44 634 38 66 Fax. +41 44 634 49 13. sek@hist.uzh.c
  3. 9 Department of Neurology, University Hospital Zurich, Zurich, Switzerland. bettina.schreiner@uzh.ch. 10 Institute of Experimental Immunology, University of Zurich, Zurich and spectral cytometry of blood and thymus samples from MG patients in combination with supervised and unsupervised machine-learning tools to gain insight into the immune.
  4. The Impact of Working Memory Training on Children's Cognitive and Noncognitive Skills (PDF, 2 MB) 2020 (with E. Berger, H. Hermes, D. Schunk, und K. Winkel) Other-regarding preferences and redistributive politics. 2019 (with Thomas Epper and Julien Senn) Behavioral Foundations of Corporate Culture (PDF, 2 MB) (2018
  5. ant analysis, decision trees, neu-ral networks, and support vector machines) and unsupervised learning techniques (such a

In this conversation. Verified account Protected Tweets @; Suggested user UZH/USZ: Institute for Clinical Chemistry: E-Mail: Dr. Fabian Wahl: Agroscope: Food Microbial Systems Research Division: E-Mail: Prof. Emma Wetter Slack: ETH: Laboratory for Food Immunology: E-Mail: Prof. Annelies Zinkernagel: UZH/USZ: Department of Infectious Diseases and Hospital Epidemiology: E-Mai Department of Political Science Affolternstrasse 56 8050 Zürich Tel: 044 634 58 35 / 044 634 38 41 Fax: 044 634 49 25 E-Mail: sekretariat@ipz.uzh.ch Opening hours Secretariat of all Chairs We are seeking enthusiastic postdoctoral scholars in Computational Biology and Machine Learning. The successful candidate will develop integrative statistical and machine learning approaches for extracting insights from cutting-edge single cell omics (e.g. CITE-seq, scATAC-seq, spatial transcriptomics) and multi-spectral imaging (e.g. CODEX, Vectra) datasets Machine learning algorithms have made a quantum leap in recent years, as was also demonstrated by Scaramuzza's small quadcopter which, loaded with a camera eye, has learned astonishing skills with training from his team

Deep Learning and Neural Machine Translation - UZ

Applied Mathematics and Statistics Department. Colorado School of Mines. 1500 Illinois St., Golden, CO 80401, USA. gerber@mines.edu. I develop cutting-edge machine learning methods and software for the analysis of large datasets The Construction of Terrorism. Combining Machine Learning and Manual Content Analysis to Identify Patterns of Media Attention towards and Presentation of Terrorism. Paper presented at the Doctoral Colloquium of the Journalism Studies Conference of the DGPuK. 18.-20. September 2019, Eichstätt. 17 The first part of this project is an empirical analysis of legal reasoning involved in trademark opposition proceedings in Switzerland (Widerspruchsverfahren).We examine a novel dataset on trademark opposition proceedings brought before the Swiss Federal Institute of Intellectual Property (IPI). In these proceedings, the likelihood of confusion between two (or more) trademarks is assessed. Tag der Lehre has taken place annually at UZH since 2009, with the aim of fostering dialogue between students and teachers and encouraging reflection on teaching and learning methods at UZH. At this year's event - the 10th edition - the focus was on digitalization

UZH - Institut für Computerlinguistik - Simon Clematide

UZH: PhD Student in computer vision and machine learnin

  1. UZH - UZH Weiterbildung - Programme nach Abschlus
  2. The Rise of Machine Learning in Marketing: Goal - UZ
  3. Advanced Machine Learnin
  4. UZH: 1 Postdoc in Machine Learnin
  5. UZH: 2 Positions Specialized in Machine Learnin
  6. UZH: Post-Doctoral Position in Machine Learnin
UZH - Department of Informatics - Blockchain-based Car DossierUZH - Institut für InformatikANTONIO LOQUERCIO | University of Zurich, Zürich | UZHUZH - DDIS - PhD Graduates
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