In the opening event, Christian Beilmann (HIDA coordinator) and Otmar D. Wiestler (President Helmholtz Association) will first welcome everyone to the 2nd Incubator Summer Academy (9 - 9:15am).
Then, Dirk Werth from the August-Wilhelm Scheer Institut will give a keynote on "Could AI become the better scientist? Understanding how digital science impacts our life in science, education and...
We get to know our programming tool and develop a fundamental understanding how writing Python code works.
Supervised exercises allow to consolidate the new knowledge.
The course package covers foundations and recent advances of machine learning techniques, including:
● Basic concepts: Linear regression, nearest neighbour, parametric vs.
non-parametric methods, Bayesian classifiers, the curse of
dimensionality, model accuracy, bias-variance trade-off
● Linear classifiers: linear regression for classification (discriminative
model), linear...
In this talk the Helmholtz Open Science Office will focus on digital reproducibility and its importance for open and robust science.
Research is reproducible when it is possible to (independently) recreate the same results from the same data and same code/analysis as used by the original researcher or team of researchers. Reproducibility enhances collaboration and transparency in science and...
The course package covers foundations and recent advances of machine learning techniques, including:
● Basic concepts: Linear regression, nearest neighbour, parametric vs.
non-parametric methods, Bayesian classifiers, the curse of
dimensionality, model accuracy, bias-variance trade-off
● Linear classifiers: linear regression for classification (discriminative
model), linear...
The course package covers foundations and recent advances of machine learning techniques, including:
● Basic concepts: Linear regression, nearest neighbour, parametric vs.
non-parametric methods, Bayesian classifiers, the curse of
dimensionality, model accuracy, bias-variance trade-off
● Linear classifiers: linear regression for classification (discriminative
model), linear...
The course package covers foundations and recent advances of machine learning techniques, including:
● Basic concepts: Linear regression, nearest neighbour, parametric vs.
non-parametric methods, Bayesian classifiers, the curse of
dimensionality, model accuracy, bias-variance trade-off
● Linear classifiers: linear regression for classification (discriminative
model), linear...
This course will introduce participants to the concepts of AI and Machine Learning, covering clustering and clasifications fundamentals as well as practical experience with standard methods for both techniques. Lastly, participants will gain an insight on best practises for evaluating a machine learning model's performance (ROC curve, FPR etc.)
We get to know additional code structures and language features and learn how to apply them for solving various problems.
Supervised exercises offer additional problems to gather experience with the learned skills.
This course will introduce participants to the concepts of AI and Machine Learning, covering clustering and clasifications fundamentals as well as practical experience with standard methods for both techniques. Lastly, participants will gain an insight on best practises for evaluating a machine learning model's performance (ROC curve, FPR etc.)
An introduction to the popular data processing framework.
Hands-on-exercises allow to solidify the gained knowledge.
This course will introduce participants to the concepts of AI and Machine Learning, covering clustering and clasifications fundamentals as well as practical experience with standard methods for both techniques. Lastly, participants will gain an insight on best practises for evaluating a machine learning model's performance (ROC curve, FPR etc.)
Learn how to use the popular plotting framework to generate graphs from data sets.
Additional exercises allow to gather experience with different kinds of visualizations..
The ReproHack reproducibility hackathon is a hands-on event where you can practice reproducible research with real-world data and research software (R or Python). We will suggest two scientific publicatios from the ReproHack repository (www.reprohack.org) with publicly available code and data you can choose from. During the event, you aim to reproduce the scientific results (figures and...
This course will introduce participants to the concepts of AI and Machine Learning, covering clustering and clasifications fundamentals as well as practical experience with standard methods for both techniques. Lastly, participants will gain an insight on best practises for evaluating a machine learning model's performance (ROC curve, FPR etc.)
The success of Machine Learning has revolutionized the field of medical image analysis in the past 5 years. This talk will give an introduction to relevant concepts in machine learning with a focus on computer vision. Subsequently, several example applications in the biomedical domain will be discussed to study the current state of research and the associated challenges and opportunities. We...
During this course participants will get an introduction to the topic of Explainable AI (XAI). The goal of the course is to help participants understand how XAI methods can help uncover biases in the data or provide interesting insights. After a general introduction to XAI, the course goes deeper into state-of-the-art model agnostic interpretation techniques as well as a practical session...
The success of Machine Learning has revolutionized the field of medical image analysis in the past 5 years. This talk will give an introduction to relevant concepts in machine learning with a focus on computer vision. Subsequently, several example applications in the biomedical domain will be discussed to study the current state of research and the associated challenges and opportunities. We...
The success of Machine Learning has revolutionized the field of medical image analysis in the past 5 years. This talk will give an introduction to relevant concepts in machine learning with a focus on computer vision. Subsequently, several example applications in the biomedical domain will be discussed to study the current state of research and the associated challenges and opportunities. We...
The success of Machine Learning has revolutionized the field of medical image analysis in the past 5 years. This talk will give an introduction to relevant concepts in machine learning with a focus on computer vision. Subsequently, several example applications in the biomedical domain will be discussed to study the current state of research and the associated challenges and opportunities. We...
This is an hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.
The use of Deep Learning has seen a sharp increase of popularity and applicability over the last decade. While Deep Learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of Deep Learning...
We get to know the basics of version control with Git. The live coding approach allows you to directly apply your new knowledge in practice.
You will learn:
- about the differences between and the importance of data & metadata
- to annotate your research data with structured metadata
- to find and evaluate a suitable metadata framework and data repository
- to use basic Markdown / JSON / XML
- which tools are already available to level up your metadata annotation game
- why structured metadata is important...
This is an hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.
The use of Deep Learning has seen a sharp increase of popularity and applicability over the last decade. While Deep Learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of Deep Learning...
We get to know the basics of GitLab as well as how to combine Git, GitLab issues and GitLab merge requests to form an effective contribution workflow. The live coding approach and the team exercise allow you to directly apply your new knowledge in practice.
You will learn:
- about the differences between and the importance of data & metadata
- to annotate your research data with structured metadata
- to find and evaluate a suitable metadata framework and data repository
- to use basic Markdown / JSON / XML
- which tools are already available to level up your metadata annotation game
- why structured metadata is important...
This is an hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.
The use of Deep Learning has seen a sharp increase of popularity and applicability over the last decade. While Deep Learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of Deep Learning...
Containerized solutions can be helpful in the testing stage of continuous integration. This day will focus on how to use containerized solutions for scientific projects using Docker as an example.
This is a hands-on session where you can bring your own (meta)data to discuss consult about your questions and challenges.
With continuous integration in GitLab, you can automate the building, testing, and deploying of your code. This day will focus on creating an initial GitLab CI pipeline.
In the course, we will discuss visualization of spatial datasets in general and then transform 3D segmentations into effectful Blender renderings.
In this course, we are going to provide the participants with knowledge about the typical mathematical tasks and caveats of image reconstruction problems. This covers advanced forward models and uncertainty, regularizing the reconstruction in order to prevent artifacts caused by noisy data and model errors, and eventually computational tasks. The participants will get the chance to test...
Building on day 2, you will learn advanced concepts of GitLab CI useful for optimizing the pipeline.
