This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines.

This unique text will enable readers to understand the fundamental theory, current techniques, and potential applications of Cloud Radio Access Networks (C-RANs). Leading experts from academia and industry provide a guide to all of the key elements of C-RANs, including system architecture, performance analysis, technologies in both physical and medium access control layers, self-organizing and green networking, standards development, and standardization perspectives. Recent developments in the field are covered, as well as open research challenges and possible future directions. The first book to focus exclusively on Cloud Radio Access Networks, this is essential reading for engineers in academia and industry working on future wireless networks.

Experience a guided tour of the key information-theoretic principles that underpin the design of next-generation cellular systems with this invaluable reference. Written by experts in the field, the text encompasses principled theoretical guidelines for the design and performance analysis of network architectures, coding and modulation schemes, and communication protocols. Presenting an extensive overview of the most important ideas and topics necessary for the development of future wireless systems, as well as providing a detailed introduction to network information theory, this is the perfect tool for researchers and graduate students in the fields of information theory and wireless communications, as well as for practitioners in the telecommunications industry.

This monograph is motivated by a number of recent developments that appear to define a possible new role for researchers with an engineering profile. First, there are now several software libraries - such as IBM's Qiskit, Google's Cirq, and Xanadu's PennyLane - that make programming quantum algorithms more accessible, while also providing cloud-based access to actual quantum computers. Second, a new framework is emerging for programming quantum algorithms to be run on current quantum hardware: quantum machine learning. In the current noisy intermediate-scale quantum (NISQ) era, quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers. In quantum machine learning, the gates of a quantum circuit are parametrized, and the parameters are tuned via classical optimization based on data and on measurements of the outputs of the circuit. Parametrized quantum circuits (PQCs) can efficiently address combinatorial optimization problems, implement probabilistic generative models, and carry out inference (classification and regression).This monograph provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra. It first describes the background, concepts, and tools necessary to describe quantum operations and measurements. Then, it covers parametrized quantum circuits, the variational quantum eigensolver, as well as unsupervised and supervised quantum machine learning formulations.

There is a wealth of literature and books available to engineers starting to understand what machine learning is and how it can be used in their everyday work. This presents the problem of where the engineer should start. The answer is often "for a general, but slightly outdated introduction, read this book; for a detailed survey of methods based on probabilistic models, check this reference; to learn about statistical learning, this text is useful" and so on. This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment, encompassing recent developments and pointers to the literature for further study. A Brief Introduction to Machine Learning for Engineers is the entry point to machine learning for students, practitioners, and researchers with an engineering background in probability and linear algebra.