• Title: Machine Learning for Cyber Physical Systems
• Author(s) Jurgen Beyerer (Editor), Christian Kuhnert (Editor), Oliver Niggemann (Editor)
• Publisher: Springer Vieweg; 1st ed.; eBook (Creative Commons Licensed)
• License(s): Creative Commons License (CC)
• Hardcover: 233 pages
• eBook: PDF
• Language: English
• ISBN-10: 3662584840
• ISBN-13: 978-3662584842
• Share This:  

Book Description

This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems (CPS), experiences and visions.

Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

• Jurgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology.

• Amazon

• Machine Learning
• Neural Networks and Deep Learning
• Artificial Intelligence
• Data Analysis and Data Mining

• Machine Learning for Cyber Physical Systems (Jurgen Beyerer, et al)
• The Mirror Site (1) - PDF

• Cyber-Physical Systems: A Model-Based Approach

  Offers a unique perspective to gain the skills needed for learning, applying, and developing a wide range of scientific, engineering, and social knowledge that are key to engineering future systems.

• Fairness and Machine Learning: Limitations and Opportunities

  This book is an introduction to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making.

• The Hundred-Page Machine Learning Book (Andriy Burkov)

  Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.

• Introduction to Statistical Learning: with Applications in Python

  This book covers the same materials as Introduction to Statistical Learning: with Applications in R (ISLR) but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

• The Shallow and the Deep: Old School Machine Learning

  This book is a collection of lecture notes that offers an accessible introduction to Neural Networks and machine learning in general. The focus lies on classical machine learning techniques, with a bias towards classification and regression.

• Financial Machine Learning (Bryan T. Kelly, et al.)

  This book is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.

• Distributional Reinforcement Learning (Marc G. Bellemare, et al)

  Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. This first comprehensive guide provides a new mathematical formalism for thinking about decisions from a probabilistic perspective.

• Computational Formalism: Art History and Machine Learning

  How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another.

• Machine Learning under Resource Constraints (Katharina Morik)

  It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering.

• An Introduction to Machine Learning Interpretability

  Understanding and trusting models and their results is a hallmark of good science. Get an applied perspective on how this applies to machine learning, including fairness, accountability, transparency, and explainable AI.

• Hyperparameter Tuning for Machine Learning: A Practical Guide

  This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods.

• Approaching (Almost) Any Machine Learning Problem

  This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.

• An Introduction to Quantum Machine Learning for Engineers

  This book provides a self-contained introduction to Quantum Machine Learning for an audience of engineers with a background in probability and linear algebra, describes the necessary background, concepts, and tools, covers parametrized quantum circuits, etc.

• Mathematics for Machine Learning (Marc P. Deisenroth, et al.)

  This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It provides a beautiful exposition of the mathematics underpinning modern machine learning.

• Mathematics for CS and Machine Learning (Jean Gallier, et al.)

  Covering everything you need to know about machine learning, now you can master the mathematics, computer science and statistics behind this field and develop your very own neural networks!

• Metalearning: Applications to Automated Machine Learning

  This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and Automated Machine Learning (AutoML). It can help developers to develop systems that can improve themselves through experience.

• An Introduction to Deep Reinforcement Learning

  Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine.

• Probabilistic Machine Learning: An Introduction (Kevin Murphy)

  This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

• Probabilistic Machine Learning: Advanced Topics (Kevin Murphy)

  This book expands the scope of Machine Learning to encompass more challenging problems, discusses methods for discovering 'insights' about data, and how to use probabilistic models for causal inference and decision making under uncertainty.

• The Big Book of Machine Learning Use Cases

  This how-to reference guide provides everything you need - including code samples and notebooks - to start putting Machine Learning to work. It's a collection of technical blogs from industry thought leaders with practical use cases you can leverage today.

• Pen and Paper Exercises in Machine Learning (Michael Gutmann)

  This is a collection of (mostly) pen-and-paper exercises in machine learning. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning.