update

content/07_hub/0_intro.md

 # HUB
 
 `!!!! under construction !!!!`
-`features that need be added: search for keywords`
+
+`features that need be added:` 
+- `search for keywords`
+- `singularity files for colab for external users`
 
 
 The HUB provides a collection of teaching materials in the form of microcredits. A microcredit is a teaching unit that counts 0.25 - 1.0 ECTS. You may bundle a collection of different microcredits and bring them in as part of the interdisciplinary pool qualifications. 
 
-This could be the basic workflow:
-- Browse the below collection of microcredits
-- clone the exercise (jupyternotebook) onto the cluster (only for TU Braunschweig students with a valid GITZ ID!)
+```{figure} ../../figures/hub.png
+---
+align: center
+---
+The HUB brings together students and lecturers from different displicines in order to foster interdisciplinary education and research.
+```
+
+We invite you to ...
+- browse the provided collection of microcredits
+- clone the microcredits onto the cluster (only for TU Braunschweig students with a valid GITZ ID!) or any other machine
 - complete the assigments. 
-- If a report or oral examination is required, contact the responsible lecturer. 
+- contact the responsible lecturer, if a report or assessment is required.
 
 **Need help?** Contact your lecturers to recommend you tasks that match your individual level of knowledge and personal interests!
 
-The possible extent of the microcredits below is as follows:
+The extent of the microcredits is as follows:
 
 | ECTS            | 0.25       | 0.5        | 0.75       | 1.0        |
 | --------------- | ---------- | ---------- | ---------- | ---------- |
 | Working hours   | 6 - 7.5    | 12.5 - 15  | 18-22.5    | 25-30      |
 
 
-```{figure} ../../figures/hub.png
----
-align: center
----
-The HUB brings together students and lecturers from different displicines in order to foster interdisciplinary education and research.
-```
+
 
 
 ## For Lecturers
@@ -41,7 +46,7 @@ Short description of microcredit (max. 700 characters).
 git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
 \`\`\`
 
-**Extent: 0.25/0.5/0.75/1 (ECTS??)**\
+**Extent: 0.25/0.5/0.75/1 ECTS**\
 **Responsible: Institute, University**
 ````
 

content/07_hub/2_ml_basics.md

 # Machine learning basics
 
+## Gaussian Processes
+
+Gaussian processes, also known as `Kriging`, are probabilistic models used in machine learning and statistics. They provide a flexible framework for modeling and `predicting functions or distributions`. A Gaussian is characterized by a `mean function and a covariance function (kernel)`, which determines the smoothness and correlation properties of the functions. Gaussian processes are widely used in regression, interpolation, and optimization tasks, providing uncertainty estimates along with predictions. They offer a `non-parametric approach` that can handle complex data and are particularly useful when limited data is available.
+
+```console
+git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/gaussian-processes.git
+```
+
+**Extent: 1 ECTS**\
+**Responsible: iRMB, TU BS**
+## Genetic Algorithms
+
+Genetic algorithms are `gradient-free optimization` techniques inspired by evolution. They start with a set of potential solutions represented as individuals. These individuals have genes (parameters) that encode solutions. Through selection, crossover, and mutation, new offspring are created. The fittest individuals have a higher chance of being selected. This process continues until a satisfactory solution is found. They are `useful for complex problems with many possible solutions`.
+```console
+git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/genetic-algorithms.git
+```
+**Extent: 1 ECTS**\
+**Responsible: iRMB, TU BS**
 ## Machine Learning Introduction 
 Placeholder.
 ```console
@@ -7,31 +25,10 @@ git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
 ```
 **Extent: 3 Microcredits (ECTS??)**\
 **Responsible: IFN, TU BS**
-
 ## PyTorch and Tensorflow Introduction
 Placeholder.
 ```console
 git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
 ```
-
 **Extent: 3 Microcredits (ECTS??)**\
 **Responsible: IFN, TU BS**
-
-
-## Genetic Algorithms
-Placeholder.
-```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
-```
-
-**Extent: 1 ECTS**\
-**Responsible: iRMB, TU BS**
-
-## Gaussian Processes
-Placeholder.
-```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
-```
-
-**Extent: 1 ECTS**\
-**Responsible: iRMB, TU BS**

content/07_hub/2_ml_basics/2_ml_basics.md

+## Gaussian Processes
+
+Gaussian processes, also known as `Kriging`, are probabilistic models used in machine learning and statistics. They provide a flexible framework for modeling and `predicting functions or distributions`. A Gaussian is characterized by a `mean function and a covariance function (kernel)`, which determines the smoothness and correlation properties of the functions. Gaussian processes are widely used in regression, interpolation, and optimization tasks, providing uncertainty estimates along with predictions. They offer a `non-parametric approach` that can handle complex data and are particularly useful when limited data is available.
+
+```console
+git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/gaussian-processes.git
+```
+
+**Extent: 1 ECTS**\
+**Responsible: iRMB, TU BS**
+## Genetic Algorithms
+
+Genetic algorithms are `gradient-free optimization` techniques inspired by evolution. They start with a set of potential solutions represented as individuals. These individuals have genes (parameters) that encode solutions. Through selection, crossover, and mutation, new offspring are created. The fittest individuals have a higher chance of being selected. This process continues until a satisfactory solution is found. They are `useful for complex problems with many possible solutions`.
+```console
+git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/genetic-algorithms.git
+```
+**Extent: 1 ECTS**\
+**Responsible: iRMB, TU BS**
+## Machine Learning Introduction 
+Placeholder.
+```console
+git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+```
+**Extent: 3 Microcredits (ECTS??)**\
+**Responsible: IFN, TU BS**
+## PyTorch and Tensorflow Introduction
+Placeholder.
+```console
+git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+```
+**Extent: 3 Microcredits (ECTS??)**\
+**Responsible: IFN, TU BS**
\ No newline at end of file

content/07_hub/2_ml_basics/gaussian-processes-irmb.md

@@ -3,8 +3,8 @@
 Gaussian processes, also known as `Kriging`, are probabilistic models used in machine learning and statistics. They provide a flexible framework for modeling and `predicting functions or distributions`. A Gaussian is characterized by a `mean function and a covariance function (kernel)`, which determines the smoothness and correlation properties of the functions. Gaussian processes are widely used in regression, interpolation, and optimization tasks, providing uncertainty estimates along with predictions. They offer a `non-parametric approach` that can handle complex data and are particularly useful when limited data is available.
 
 ```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/gaussian-processes.git
 ```
 
 **Extent: 1 ECTS**\
-**Responsible: iRMB, TU BS**
\ No newline at end of file
+**Responsible: iRMB, TU BS**

content/07_hub/2_ml_basics/genetic-algorithms-irmb.md

@@ -2,7 +2,7 @@
 
 Genetic algorithms are `gradient-free optimization` techniques inspired by evolution. They start with a set of potential solutions represented as individuals. These individuals have genes (parameters) that encode solutions. Through selection, crossover, and mutation, new offspring are created. The fittest individuals have a higher chance of being selected. This process continues until a satisfactory solution is found. They are `useful for complex problems with many possible solutions`.
 ```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/genetic-algorithms.git
 ```
 **Extent: 1 ECTS**\
 **Responsible: iRMB, TU BS**

content/07_hub/2_ml_basics/pytorch-tf-intro-ifn.md

@@ -4,4 +4,4 @@ Placeholder.
 git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
 ```
 **Extent: 3 Microcredits (ECTS??)**\
-**Responsible: IFN, TU BS**
\ No newline at end of file
+**Responsible: IFN, TU BS**\

content/07_hub/3_datadrivenmodeling.md

@@ -21,7 +21,7 @@ In this small project, you will familiarize yourself with this approach and
 solve a simple steady-state heat equation.
 
 ```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/physics-informed-neural-networks.git
 ```
 **Extent: 1 ECTS**\
 **Responsible: iRMB, TU BS**

content/07_hub/3_datadrivenmodeling/pinn.md

@@ -9,7 +9,7 @@ In this small project, you will familiarize yourself with this approach and
 solve a simple steady-state heat equation.
 
 ```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/physics-informed-neural-networks.git
 ```
 **Extent: 1 ECTS**\
 **Responsible: iRMB, TU BS**