machine learning

Dr. Behnam Yousefi, Institute of medical systems biology, UKE

This workshop is for students, researchers, and clinicians keen to learn the R programming language and data analysis relevant to biomedicine. The course is designed to be practical and comprehensive with no specific background requirements. We will focus on fundamentals of data analysis with examples of real-life data in biomedicine, such as gene expression. By the end of the course, participants will be familiar with the essentials of data analysis, including statistical tests, linear regression, principal component analysis, clustering and data visualization. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics:

Basics of R programming language
Statistical tests
Linear regression
Principal component analysis (PCA)
Clustering
Data visualization

Dr. Behnam Yousefi, Institute of medical systems biology, UKE

This workshop is for students, researchers, and clinicians keen to learn the R programming language and data analysis relevant to biomedicine. The course is designed to be practical and comprehensive with no specific background requirements. We will focus on fundamentals of data analysis with examples of real-life data in biomedicine, such as gene expression. By the end of the course, participants will be familiar with the essentials of data analysis, including statistical tests, linear regression, principal component analysis, clustering and data visualization. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics:
Basics of R programming language
Statistical tests
Linear regression
Principal component analysis (PCA)
Clustering
Data visualization

Dr. Behnam Yousefi, Institute of medical systems biology, UKE

This workshop is open to students, researchers, and clinicians keen to learn the essentials of machine learning and implementing it via Python. The aim of the course is to provide a comprehensive map of machine learning (and deep learning) methods with no specific background requirements. A little background in python can be helpful, though. We will focus on fundamentals of machine learning, validation methods, linear and nonlinear models, and feature reduction. The students will also get familiarized with the Python packages of Sci-kit Learn and Pytorch. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics
Types of machine learning: supervised and unsupervised
Validation  metrics and cross validation
Introduction to linear and nonlinear models include: Linear regression, Random forest, support vector machines, deep neural networks.
Feature reduction.
Regularization.

learn_bAIome offers workshops and trainings in biomedical AI/data science with tailored formats that take into account background, programming skills and intensity to provide unique, focused, and effective courses. These courses are free and open to students, clinicians, and researchers across academic institutions in Hamburg.

Lecturer: Dr. Fatemeh Hadäghi, Institute of Computational Neuroscience, UKE

Prerequisites A basic understanding of neural networks and machine learning concepts is expected as well as a familiarity with Python and basic programming skills.

Description This workshop is open to students, researchers, and clinicians wanting to learn about recurrent neural networks (RNNs) and their applications in biomedical signal processing. RNNs are vital tools in the field of neural networks, especially known for their capability to manage sequential data. This workshop will provide an accessible introduction to RNNs, concentrating on their core concepts and various applications. We will explore how RNNs excel at capturing temporal dependencies through their unique recurrent connections, making them highly effective for a variety of tasks. Participants can expect to achieve a solid understanding of the basic principles and architecture of RNNs as well as the ability to identify suitable applications for RNNs and implement basic RNN models. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics

• Overview of RNN fundamentals and how they differ from other neural networks
• Key applications of RNNs in biomedical signal processing
• Reservoir computing (RC)
• Hands-on exercises and examples to illustrate RNN implementation and usage

Die Veranstaltungsreihe beschäftigt sich in diesem Herbst unter dem Titel KI und Wir mit den rasanten und grundlegenden Veränderungen unserer Gesellschaft durch künstliche Intelligenz.

Vom 9. Oktober bis zum 14. November diskutieren Expert:innen aus Wissenschaft, Politik und Praxis mit dem Hamburger Publikum über das Potenzial von ChatGPT und Co. Wie können die neuen Technologien Medizin und Klimaschutz voranbringen oder auch in der Kunst genutzt werden? Wo kann KI beim Lernen in Schule, Hochschule und darüber hinaus unterstützen? Welche Regulierungen oder Transparenzvorschriften brauchen wir dabei? Um diese und andere Fragen geht es in vielfältigen Formaten von Podiumsdiskussionen über Ausstellungen bis hin zu interaktiven Workshops, die zum

Mitdiskutieren, Dazulernen und Ausprobieren einladen. Die Veranstaltungen finden u.a. an der Uni Hamburg statt. Viele UHH-Mitglieder tragen aktiv zum Programm bei.

Institutions

• UHH
• HIAS
• Körber Stiftung

learn_bAIome offers workshops and trainings in biomedical AI/data science with tailored formats that take into account background, programming skills and intensity to provide unique, focused, and effective courses. These courses are free and open to students, clinicians, and researchers across academic institutions in Hamburg.

This workshop is open to students, researchers, and clinicians wanting to learn how machine learning is applied for biomedical datasets, the different classes of machine learning algorithms that may be used, as well as the best practices in selecting and evaluating algorithms, and their limitations.  The aim of the course is to provide concepts and tools to navigate the use of machine learning in the biomedical landscape. The course will use biological datasets and there will be hands-on components as well as discussions. Participants should already have taken an introduction to machine learning and be familiar with Python programming. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics

• Taxonomy of machine learning algorithms
• Linear regression, logistic regression and related methods
• Decision trees
• Support Vector Machines
• Bias & Variance, curse of dimensionality
• Representation learning
• Neural networks and deep learning: MLPs, transformers, CNNs
• Applications to RNAseq and imaging data

bAIome Center for biomedical AI (UKE) and Bernhard Nocht Institute for Tropical Medicine (BNITM) will host the seminar series entitled “AI in biology and Medicine”. This series aims to capture a broad audience and promote cross institutional collaboration. Our expert speakers will give an overview and insight into particular AI/data science methods being developed in key areas of biology and medicine. We will have drinks and snacks following each seminar to facilitate exchange.

Lorenz Adlung, I Medical Clinic and Polyclinic, UKE: Machine learning of MCMV infection dynamics

For further details and hybrid links, please go to the webpage AI in Biology & Medicine

Tentative agenda for the AWS Immersion Day on May 31^st

It will be assumed that participants know basic Python, know how to work on a Jupyter Notebook, understand basic commands on libraries such as Pandas and SciKit Learn, and have a high level understanding of what is Pytorch/ Tensorflow. Each participant will be given an test AWS account that will be active until Friday afternoon so it is suggested that each participant works on its own laptop. We will do 2 labs together, but there will more labs available for those interested into doing further tests or exploring other features not covered during the 3 hours.

In order to participate, please send your first name and email address to adeline.scharfenberg@uni-hamburg.de 

On September, 25th to 27th, 2023 the third MLE Days on machine learning in engineering will take place on the campus of the Hamburg University of Technology (TUHH). This year, it combines a summer school with a one-day startup-challenge. Participation is free of charge.

The Summer School teaches insights into the world of machine learning with a focus on engineering. It provides sessions about fundamentals of machine learning, concrete application examples, as well as hands-on sessions to try out and consolidate lessons learned. Keynote talks complete the program. Three parallel tracks are provided to allow participants to choose contributions adapted to their interest and machine learning experience. The use cases range from sensor and image processing, to electrical engineering and materials science, to aviation and maritime logistics. A poster session and an elevator pitch event allow participants to present their own work on machine learning topics. The best posters and pitches will be selected by a jury and awarded prizes. A networking event allows attendees to establish contacts with selected corporate partners and sponsors from start-ups and medium-sized businesses to large corporations. In the startup-challenge attendees learn how to turn machine learning ideas into a business.

The MLE Days are organized by the Machine Learning in Engineering research initiative of the TUHH (MLE@TUHH) in collaboration with the Helmholtz Center Hereon, DASHH, and the Career Center of TUHH, the AI.Startup.Hub, and AI.HAMBURG. The MLE initiative joins the competencies in the field of machine learning at the Hamburg University of Technology with the goal of transferring knowledge towards business and industry. Students, PhD students, postdocs, and professors from all disciplines of the TUHH are engaged together with colleagues from the Helmholtz Center Hereon to make methods and applications of machine learning known, to network, and to foster scientific exchange.

Join us to discover the new applications of machine learning in engineering practice!

Institutions

• Hamburg University of Technology (TUHH) 

The Machine Learning in Engineering (MLE) initiative integrates competencies and activities in the field of machine learning at Hamburg University of Technology (TUHH) and partner organizations from the Hamburg metropolitan area. Within the Machine Learning in Engineering (MLE) initiative, students, doctoral candidates, postdocs, researchers, and professors from all departments at the TUHH and from the partners are working interdisciplinary together. The aim is to conduct fundamental research particularly relevant for the development of new technology, thereby contributing to the digital transformation of the engineering sciences. In addition to basic research, the initiative aims at transferring knowledge to business and industry. Among a number of instruments for this kind of transfer, the annually organised MLE days provide a natural opportunity for education and knowledge exchange.

Registration: If you want to attend, please, send an email to Ulrike Schneider (in cc) with your name and affiliation. The registration is open until 01.03.2025. The number of participants is limited by the size of the lecture hall. For guaranteeing your participation and to make planning (particularly of the coffee breaks) easier, please register as soon as possible. Your data will only be used for organizing the MLE Days.

Program (preliminary):
09:00-15:00: Presentations by MLE-members and partners following the updated MLE structure here   
15:00-17:00: Poster & Networking session

Catering: There will be a coffee break in the morning and afternoon with hot drinks and snacks. Lunch in the canteen is to be paid for by the participants themselves.

Poster in English until 14.02.2025.

The organizing committee reserves the right to reject registrations and poster contributions. If you have any questions, please do not hesitate to contact the organizing team via mle@tuhh.de .

Institutions

• Hamburg University of Technology (TUHH) 

Explore the transformative potential of the Population Dynamics Foundation Model (PDFM), a cutting-edge AI model designed to capture complex, multidimensional interactions among human behaviors, environmental factors, and local contexts. This workshop provides an in-depth introduction to PDFM Embeddings and their applications in geospatial analysis, public health, and socioeconomic modeling. 

Participants will gain hands-on experience with PDFM Embeddings to perform advanced geospatial predictions and analyses while ensuring privacy through the use of aggregated data. Key components of the workshop include: 

• Introduction to PDFM Embeddings: Delve into the model architecture of PDFM and discover how aggregated data (such as search trends, busyness levels, and weather conditions) generates location-specific embeddings.
• Data Preparation: Learn to integrate ground truth data, including health statistics and socioeconomic indicators, with PDFM Embeddings at the postal code or county level.
• Hands-On Exercises: Engage with interactive Colab notebooks to explore real-world applications, such as predicting housing prices using Zillow data and nighttime light predictions with Google Earth Engine data.
• Visualization and Interpretation: Analyze and visualize geospatial predictions and PDFM features in 3D, enhancing your ability to interpret complex datasets. 

By the end of this workshop, participants will have a strong foundation in utilizing PDFM Embeddings to address real-world geospatial challenges. 

With the launch of ChatGPT last year and the ensuing debate about the benefits and potential risks of generative AI, also the work on the European AI Act shifted into a higher gear. The European Council and Parliament, working on their respective compromise texts, had to find ways to accommodate this new phenomenon. The attempts to adapt the AI Act went hand in hand with a lively public debate on what was so new and different about generative AI, whether it raised new, not yet anticipated risks, and how to best address a technology whose societal implications are not yet well understood. Most importantly, was the AI Act outdated even before is adopted? In my presentation I would like to discuss the different approaches that the Council and Parliament adopted to governing Generative AI, the most salient points of discussion and the different approaches proposed to solve some of the key ethical and societal concerns around the rise of generative AI.

Prof. Dr. Natali Helberger (Universiteit van Amsterdam, NL)
Natali Helberger is Distinguished University Professor of Law and Digital Technology, with a special focus on AI, at the University of Amsterdam and a member of the Institute for Information Law (IViR). Her research on AI and automated decision systems focuses on its impact on society and governance. Helberger co-founded the Research Priority Area Information, Communication, and the Data Society, which has played a leading role in shaping the international discussion on digital communication and platform governance. She is a founding member of the Human(e) AI research program and leads the Digital Transformation Initiative at the Faculty of Law. Since 2021, Helberger has also been director of the AI, Media & Democracy Lab, and since 2022, scientific director of the Algosoc (Public Values in the Algorithmic Society) Gravitation Consortium. A major focus of the Algosoc program is to mentor and train the next generation of interdisciplinary researchers. She is a member of several national and international research groups and committees, including the Council of Europe's Expert Group on AI and Freedom of Expression.

Institutions

• UHH

bAIome Center for biomedical AI (UKE) and Bernhard Nocht Institute for Tropical Medicine (BNITM) will host the seminar series entitled “AI in biology and Medicine”. This series aims to capture a broad audience and promote cross institutional collaboration. Our expert speakers will give an overview and insight into particular AI/data science methods being developed in key areas of biology and medicine. We will have drinks and snacks following each seminar to facilitate exchange.

Fatemeh Hadaeghi,Institute of Computational Neuroscience, UKE

For further details and hybrid links, please go to the webpage AI in Biology & Medicine