Dieses Kapitel gibt eine Einführung in den Bereich des maschinellen Lernens zum Zwecke der Unterstützung von Gebäudesimulationen und der Optimierung von Gebäudeentwürfen aus Sicht eines Anwenders von Gebäudesimulationssoftware. Der folgende …

Weather forecasting plays a significant role in the field of agriculture, energy industry, forestry and provides stable habitat for a wide variety of species. The prediction methods play a vital role in accurate precipitation forecasts. This …

Die Trennung von Artificial Intelligence, Machine Learning und Deep Learning als unabhängige Techniken ist eine Voraussetzung für das Data Management. Die Geschichte der künstlichen Intelligenz ist wichtig, um sie in Bezug auf das Data Management …

Ein Roboter, der selbstständig lernt: Für die einen faszinierend und erstrebenswert, für andere ein Horrorszenario. Dabei ist „machine learning“ nur ein Oberbegriff für statische Optimierungsverfahren, dessen Fehleinschätzung als „Lernen“ zu …

In the realm of artificial intelligence (AI), machine learning is a domain where machines learn from data and refine their performance over time. This chapter embarks on a journey into the realm of machine learning. The chapter begins by …

This contribution addresses links between machine learning technologies and democracy with a focus on political participation. Democracy research often regards machine learning technologies as a threat, as these technologies could violate …

With the increasing amount of various data types, machine learning methods capable of leveraging diverse sources of information have become highly relevant. Deep learning-based approaches have made significant progress in learning from texts and …

Machine learning (ML), it is sometimes claimed, will change the world. In this chapter, we argue that ML does offer great potential for advancing the field of cliometrics. We first attempt to demystify ML by describing its most widely used methods …

Predicting stock market trends is challenging due to the uncertainty and multiple factors that influence stock prices. This project uses deep learning and machine learning techniques to predict stock market trends. Based on stock market data from …

This chapter provides an overview of the field of machine learning, focusing on the historical developments that have led to its current state. Beginning with the conceptual breakthroughs from over 60 years ago, the chapter discusses key …

This chapter traces the evolution of machine learning (ML), from its early theoretical foundations to its contemporary applications in various scientific disciplines. Starting with Alan Turing’s seminal work and the development of the …

This chapter explores the challenges and solutions associated with unbalanced datasets in machine learning. It begins by defining what constitutes an unbalanced dataset and emphasises their prevalence. The chapter introduces the concept of machine …

This chapter delves into the critical role of statistics and probability in machine learning, starting with an overview of random experiments and variables. It progresses to cover essential topics such as set theory, probability, conditional …

This chapter delves into the fundamental concepts of calculus and optimisation related to machine learning, offering both theoretical insights and practical usecases. Starting with the motivation behind using calculus in machine learning, the …

This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories: 1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics …

Software defect prediction is a crucial discipline within the software development life cycle. Accurate identification of defective modules in software can result in time and cost savings for developers. The ELM algorithm offers the benefits of …

This review article explores the advancements in using machine learning algorithms to forecast the stock market. Various techniques are used to select features, evaluate models, and pre-process data. Examples of machine learning models discussed …

Machine learning has achieved notable advancements in the medical field by improving the accuracy, precision, and analysis of diagnostics while also reducing labour-intensive tasks. With strong evidence, machine learning has exhibited the …

Machine learning is the process of automating and analyzing patterns within data and using these identified patterns to make predictions. Supervised learning focuses on scenarios where a labeled dataset is used to train and test the machine and …