Now Publishers A Brief Introduction to Machine Learning for Engineers (Foundations and Trends (R) in Signal Processing)

Machine Learning for Engineers

Cambridge University Press

Model-Based Deep Learning (Foundations and Trends® in Signal Processing)

Machine Learning for Engineers: Using data to solve problems for physical systems

Springer

Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications

An Introduction to Quantum Machine Learning for Engineers (Foundations and Trends® in Signal Processing)

Machine Learning

Springer

Machine Learning

Springer

Non-convex Optimization for Machine Learning (Foundations and Trends in Machine Learning)

Now Publishers

Machine Learning for Civil and Environmental Engineers: A Practical Approach to Data-Driven Analysis, Explainability, and Causality

Wiley

An Introduction to Pattern Recognition and Machine Learning

Springer

Introduction to Machine Learning: From Math to Code

Cambridge University Press

Fundamentals of Machine Learning and Deep Learning in Medicine

Springer

Machine Learning for Science and Engineering: Volume I: Fundamentals (Course Notes)

Machine Learning – A Probabilistic Perspective

MIT Press

Pattern Recognition and Machine Learning (Information Science and Statistics)

Springer

Signals, Instrumentation, Control, and Machine Learning: An Integrative Introduction

Scientific Publishing

Machine Learning Crash Course for Engineers

Springer

Pattern Recognition and Machine Learning (Information Science and Statistics)

Springer

An Elementary Introduction to Statistical Learning Theory (Wiley Series in Probability and Statistics)

Wiley

Elements of Dimensionality Reduction and Manifold Learning

Springer

Machine Learning with R

Springer

Machine Learning: A Practical Approach on the Statistical Learning Theory

Springer

Fundamentals of Machine Learning

Oxford University Press

Introduction to Random Processes in Engineering

Wiley-Blackwell