Dieses Buch richtet sich an alle, welche die enormen Potenziale maschinellen Lernens f�r wissenschaftliche Fragestellungen und innovative Ans�tze in Studium oder Beruf nutzen m�chten. Denn maschinelles Lernen er�ffnet neue M�glichkeiten zum effizienten Umgang mit umfassenden, komplex strukturierten und sich schnell entwickelnden Daten. Zun�chst werden Grundideen und typische Anwendungsfelder maschinellen Lernens sowie dessen Vorz�ge gegen�ber inferenzstatistischen Verfahren erl�utert. Daran schlie�en praktische Hinweise dazu an, wie Daten f�r maschinelle Lernprozesse aufbereitet werden und wie diese durch Anpassung verschiedener Parameter m�glichst optimale Ergebnisse erzielen k�nnen. Von den hierzu einsetzbaren Modellen werden die g�ngigsten theoretisch und anhand anschaulicher Beispiele vorgestellt. Auch auf verschiedene Optionen zur besseren Interpretierbarkeit sowie auf spezifische Limitationen von Analyseresultaten wird eingegangen. Weiterf�hrende Anwendungsf�lle und verst�ndlich kommentierte Analysecodes sind auf dem GitHub-Repositorium zu diesem Buch auf SpringerLink online verf�gbar.

About the Author

Sven Hilbert ist Inhaber des Lehrstuhls Educational Data Science an der Universit�t Regensburg.

Elisabeth Kraus ist Juniorprofessorin f�r Methoden der Empirischen Bildungsforschung am Hector-Institut f�r Empirische Bildungsforschung an der Eberhard Karls Universit�t T�bingen.

Alfred Lindl leitet die interdisziplin�re Forschungsgruppe FALKO-PV (Fachspezifische Lehrkraftkompetenzen – Pr�diktive Validierung) am Lehrstuhl Educational Data Science an der Universit�t Regensburg.

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