Machine Learning

The Basics

AvAlexander Jung

Häftad, Engelska, 2023

Del i serien Machine Learning: Foundations, Methodologies, and Applications

666 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Fler format och utgåvor

Inbunden

666 kr

Skickas inom 10-15 vardagar

Beskrivning

The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods. The book’s three-component approach to ML provides uniform coverage of a wide range of concepts and techniques.

Produktinformation

Utgivningsdatum:2023-01-23
Mått:155 x 235 x 13 mm
Vikt:359 g
Format:Häftad
Språk:Engelska
Serie:Machine Learning: Foundations, Methodologies, and Applications
Antal sidor:212
Förlag:Springer Verlag, Singapore
ISBN:9789811681950

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT
Artificiell intelligens inom Data och IT

Mer om författaren

Alexander Jung is Assistant Professor of Machine Learning at the Department of Computer Science, Aalto University where he leads the research group "Machine Learning for Big Data". His courses on machine learning, artificial intelligence, and convex optimization are among the most popular courses offered at Aalto University. He received a Best Student Paper Award at the premium signal processing conference IEEE ICASSP in 2011, an Amazon Web Services Machine Learning Award in 2018, and was elected as Teacher of the Year by the Department of Computer Science in 2018. He serves as an Associate Editor for the IEEE Signal Processing Letters.

Recensioner i media

“The book under review matured from lecture notes … . The structure and style strongly recommend the book as a thorough entry point for studying ML; the exercises and the references concluding each chapter reinforce concepts and also provide a curated guidance for further steps. … The (text)book reaches a balance between mathematical details, overview of algorithms and examples, making it suitable for a wide range of readers, and further underlining the interdisciplinary character of machine learning.” (Irina Ioana Mohorianu, zbMATH 1530.68002, 2024)

Innehållsförteckning

Introduction.- Components of ML.- The Landscape of ML.- Empirical Risk Minimization.- Gradient-Based Learning.- Model Validation and Selection.- Regularization.- Clustering.- Feature Learning.- Transparant and Explainable ML.