Machine Learning Basics#

Gaussian Processes#

Content:
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.

Learning Objectives:
The students are familiar with the Gausian processes, predicting functions or distributions and difference between the mean function and a covariance function.

Link to the repository:
Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/gaussian-processes.git or

git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/gaussian-processes.git

Previous Microcredits: Machine Learning Introduction

Extent: 1 ECTS
Responsible: iRMB, TU BS

Genetic Algorithms#

Content:
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.

Learning Objectives:
The students are familiar with Genetic algorithmsand their usefulness for complex problems with many possible solutions.

Link to the repository:
Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/genetic-algorithms.git or

git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/genetic-algorithms.git

Previous Microcredits: Machine Learning Introduction, PyTorch and Tensorflow Introduction

Extent: 1 ECTS
Responsible: iRMB, TU BS

Introduction to Artificial Intelligence in Biomedicine#

Content:
This course introduces students to important concepts related to Artificial Intelligence applications in the context of biomedical data.

Learning Objectives:
Students will learn about the special features of biomedical data, bias-variance tradeoff, and the importance of interpretability and reproducibility in this field.

Link to the repository:
Check out https://git.rz.tu-bs.de/my-name-space/my-repo.git or

git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git

Previous Microcredits: Machine Learning Introduction

Extent: 1 ECTS
Responsible: Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig

Introduction to Federated Machine Learning#

Content:
After this course, students will be familiar with the basic concepts of federated machine learning and its use for clinical data.

Learning Objectives:
Students will learn about distributed data and data protection measures, as well as important techniques such as federated linear regression and federated random forests.

Link to the repository:
Check out https://git.rz.tu-bs.de/scibiome/ki4all/introduction-to-federated-machine-learning.git or

git clone https://git.rz.tu-bs.de/scibiome/ki4all/introduction-to-federated-machine-learning.git

Previous Microcredits: Machine Learning Introduction

Extent: 1 ECTS
Responsible: Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig

Einführung in Regression und Backpropagation#

Inhalt:
Am Beispiel eines Zugversuchs wird die Architektur von neuronalen Netzen einfach erklärt. Ausgehend vom linear elastischen Materialverhalten wird das Perceptron als elementarer Baustein neuronaler Netze eingeführt. Aus der Kombination zweier dieser elementaren Bausteine folgt das Multi-Layer-Perceptron. Am Beispiel der Backpropagation wird aufgezeigt, warum die breite Nutzung von maschinellen Lernverfahren eine effiziente Softwareimplementierung bedingt, die unter anderem auf der Methode der Objektorientierung basiert. Hierfür wird ein Template bereitgestellt, das für die Bearbeitung der Programmieraufgaben zur Verfügung steht. Neben den verschiedenen Ansätzen (lineare Regression, Perceptron, Multi-Layer-Perceptron) sollen Ableitungen der Zielfunktion bzgl. der Parameter des Perceptrons mittels Backpropagation implementiert werden.

Qualifikationsziele:
Die Studierenden erhalten durch verschiedene Ansätze ein umfangreiches Verständnis für die Architektur von neuronalen Netzen.

Link zum Repository:
Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/regression-ml.git or

git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/regression-ml.git

Previous Microcredits: Machine Learning Introduction

Extent: 0.5 ECTS
Responsible: iRMB, TU BS

Machine Learning Introduction#

Content:
Use of single-layer machine learning models to solve a two-class problem: support vector machines (based on libsvm) compared to a neural network. Partitioning and use of datasets, application of appropriate metrics for evaluation, use of high-level machine learning libraries such as SciKit-Learn.

Learning Objectives:
Students assess the effectiveness of simple machine learning models and neural networks for classification and regression problems.

Link to the repository:
Check out https://git.rz.tu-bs.de/ifn-public/ki4all/machine-learning-introduction or

git clone https://git.rz.tu-bs.de/ifn-public/ki4all/machine-learning-introduction

Previous Microcredits: None

Extent: 0.75 ECTS
Responsible: IFN, TU BS