Machine Learning Basics

Machine Learning Basics#

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

Single-layer perceptron#

Content/Inhalt:
This document presents the motivation, definition and fundamental properties of the single-layer perceptron, which is the main building block for (nearly) all of the modern neural network and deep learning architectures. After a brief biological motivation and introduction, the model is introducted (with a presentation of the most common activation functions) and its application to simple two-class classification problems is outlined, including a discussion of its limitation to be able to handle only linearly separable classification problems. Then, the perceptron learning algorithm to estimate the synaptic weights and threshold for a single-layer perceptron with Heaviside activation function is discussed. This is followed by a presentation of how to estimate the synaptic weights and threshold for a single-layer perceptron with differentiable activation function using gradient descent to a suitably defined loss function. This is the basis for the derivation of the backpropagation algorithm (see Microcredit „Multi-layer perceptron“), which in turn is used in many modern feedforward architectures in deep learning.

Learning Objectives/Qualifikationsziele:
After reading this document, the reader should be familiar with the biological motivation of a perceptron, its fundamental components as well as important simplifications made. Furthermore, the reader should understand the mathematical formulation of a single-layer perceptron and be able to apply it to calculate its output for a given input. In addition, he should understand the limitation of a single-layer perceptron with regard to two-class classification problems, in that it is applicable only to classification problems which are linearly separable. The reader should be further able to apply the perceptron learning algorithm to simple, linearly separable two-class classification problems and know about the perceptron convergence theorem. Then, he should understand the concept of estimating the synaptic weights and the threshold for a single-layer perceptron with differentiable activation function by minimizing a suitable loss function using gradient descent.

Link to the repositories/Link zum Repository:
https://gitlab.gwdg.de/ki4all/ohm2_slp.git or

git clone https://gitlab.gwdg.de/ki4all/ohm2_slp.git

Next microcredits/Fortsetzungsempfehlung: -

Extent/Arbeitsumfang: 0.5 ECTS
Responsible/Verantwortlichkeit: IIE, Ostfalia, Fakultät Informatik, Dr. C. Meyer

Brief introduction and history of artificial intelligence#

Content/Inhalt:
This document contains a short introduction and a brief history of Artificial Intelligence (AI) by summarizing major milestones and technological approaches from the birth of AI in the 1950s until 2020. Finally, some important strengths of AI as well as limitations are listed. Note, however, that the mentioned technological approaches cannot be explained in detail in this document. The reader is referred to further textbooks or other material in case a more detailed understanding is desired.

Learning Objectives/Qualifikationsziele:
After reading this document, the reader should understand the relevance and importance of AI, be able to give different definitions of AI, explain the distinction between weak and strong AI and outline important application fields of AI. Furthermore, the reader should be able to give a brief sketch of important episodes in the history of AI along with important events and technological approaches that shaped the field and lead to a huge diversity of algorithmic approaches within AI. Finally, the reader should know about strengths, problems and limitations of AI.

Link to the repositories/Link zum Repository:
https://gitlab.gwdg.de/ki4all/ohm1_bihai.git or

git clone https://gitlab.gwdg.de/ki4all/ohm1_bihai.git

Extent/Arbeitsumfang: 0.25 ECTS
Responsible/Verantwortlichkeit: IIE, Ostfalia, Fakultät Informatik, Dr. C. Meyer

CNN Basics#

Content/Inhalt:
This microcredit gives insight into the building blocks of convolutional neural networks.

Learning Objectives/Qualifikationsziele:
After completion students

  • know the purpose of various building blocks of CNNs;

  • can explain the function of individual building blocks;

  • are able to get information out of different architectures by means of ‘debugging’

Link to the repositories/Link zum Repository:
https://gitlab.gwdg.de/ki4all/ohm4_cnn.git or

git clone https://gitlab.gwdg.de/ki4all/ohm4_cnn.git

Previous microcredits/Empfohlene Voraussetzung: Multi-layer perceptron

Next microcredits/Fortsetzungsempfehlung: -

Extent/Arbeitsumfang: 1 ECTS
Responsible/Verantwortlichkeit: IIE, Ostfalia, Fakultät Informatik, Dr. C. Meyer

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