machine learning

Get to know other researchers working in Image computation and AI within UKE and at institutions around Hamburg.

Our workshop will consist of a mixture of elevator pitches and discussions with interesting formats to create dynamic participation. Please indicate if you are willing to give a short pitch (max 3 mins, 1 slide) of your research topic/interests.

We will have drinks and snacks throughout the afternoon and will end the workshop with pizza and drinks.

Registration: Please write to a.reinicke-vogt@uke.de by May 23^rd with your name, email, research topic, and if you are willing to give a short (3 mins, 1 slide) elevator pitch of your research.

The School of Electrical Engineering, Computer Science and Mathematics at the Hamburg University of Technology (TUHH) is pleased to share another inaugural lecture and research talk from the field of Data Science with the entire TUHH and with the public as part of its colloquium.

Program:

• Welcome
  Prof. Dr.-Ing. Irina Smirnova, Vizepräsidentin Forschung, TUHH
  Prof. Dr.-Ing. Gerhard Bauch, Studiendekan EIM, TUHH
• Inaugural Lecture “From obeying AIs to collaborative agents: a human-centric view“
  Dr. Pierre-Alexandre Murena, Human-Centric Machine Learning Institute, TUHH
• Guest talk “Towards machines that understand people”
  Dr. Andrew Howes, University of Exeter
• Get together
  Foyer, Building II, TUHH Campus

Online participation via Zoom is also possible. You will get the zoom link after registration

Once again, the five platforms Helmholtz.AI, Helmholtz Imaging, HIFIS, HIDA and HMC have teamed up to createa second edition of the Incubator Summer Academy on 18-29 September 2023! We have designed a joint program with a variety course packages covering state of the art Data Science methods and skills, as well as networking opportunities in our Summer Academy Gathertown space!

Ranging from fundamental course packages as for instance “Python”, or “Introduction to Scienctific Metadata” to advanced topics as “Machine Learning Based Image analysis”, the program offers participants to select course packages that best suit their experience levels and interests.

The Incubator Summer Academy is open to all doctoral and postdoctoral researchers in the Helmholtz Association. Additionally, a small number of seats in our workshops are reserved for Master students, doctoral and postdoctoral students from other research institutions and universities.

More detailed information and registration: https://events.hifis.net

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

In the century between the 1870s and the 1970s, hundreds of Malay-language periodicals circulated around the Malay-speaking world. These periodicals chronicle a fascinating era and have been the focus of intense study by scholars such as William Roff and Ian Proudfoot. Many of these periodicals have been digitized, and comprehensive collections are at the National Library of Singapore, as well as in other libraries and archives.
Given the availability and size of the collections, the opportunity is ripe for systematic digital analysis. Projects elsewhere in the world have demonstrated the power of analysing historical newspapers at scale using computational methods. Examples include "Living With Machines" (a partnership between the British Library and several universities in the UK) and "Oceanic Exchanges" (a partnership between Finland, Germany, Mexico, the Netherlands, the United Kingdom, and the United States).

What is holding back a similar study of Malay-language newspapers?
  – The main obstacle is the script. The majority of these periodicals were published in Jawi, an adaptation of the Perso-Arabic script for the Malay language, which poses significant challenges for digital processing. For one thing, typical Optical Character Recognition pipelines (OCR) don't work well for Jawi.
  – Another challenge is that most contemporary Malay readers, including many historians who would be interested in these collections, are less familiar with Jawi than with Rumi (the Romanized version of Malay most commonly used today). The automatic transliteration from Jawi to Rumi is also a complex task, as vowels are often not marked down in Jawi. 
  – In addition to this, spelling conventions have changed, and there are many different approaches for transliterating the same word.

To address these challenges, the "Computational Heritage" research group at the National University of Singapore has developed specialized AI models for both Jawi OCR and Jawi-to-Rumi transliteration. In this talk, I will describe our progress so far, the challenges we still face and the future directions of our work.

Institutions

• Department for Languages and Cultures of Southeast Asia, Asia-Africa-Institute (AAI), UHH

Learning to Optimize: How Machine Learning Drives Decisions at Companies like Uber

Have you ever wondered why and where taxi cars wait, or how smart Uber drivers make their decisions? This talk will delve into these intriguing questions. We will explore the concept of sequential decision-making and policy classes through a hands-on approach. Using taxi services as an example, we learn how service providers can optimize their decision-making. And guess what? There´s not as much magic behind it as you might think...so prepare to be surprised!

Distinguished Lecture Series in Business Analytics & Data Science

The Distinguished Lecture Series in Business Analytics & Data Science at Kühne Logistics University (KLU) presents public lectures on selected topics in analytics, data science, and their applications across various industries, including logistics and supply chain management. Targeted at students, alumni, industry professionals, and other interested individuals, the series offers insights into the latest advancements, trends, and challenges in these fields. Attendees gain valuable knowledge on how data-driven decision-making can transform business operations and enhance efficiency in today's digital economy.

This talk explores analytic methods that prioritize theory over pure data accumulation. It deep dives into game theory by dissecting the principal-agent model to reveal hidden strategic dynamics. Ultimately, the presentation underscores that thoughtful analysis provides robust decision-making tools where data is scarce. Attendees gain valuable knowledge on how data-driven decision-making can transform business operations and enhance efficiency in today's digital economy.

Event free of charge, register here

Distinguished Lecture Series in Business Analytics & Data Science

The Distinguished Lecture Series in Business Analytics & Data Science at Kühne Logistics University (KLU) presents public lectures on selected topics in analytics, data science, and their applications across various industries, including logistics and supply chain management. Targeted at students, alumni, industry professionals, and other interested individuals, the series offers insights into the latest advancements, trends, and challenges in these fields. Attendees gain valuable knowledge on how data-driven decision-making can transform business operations and enhance efficiency in today's digital economy.

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