A Brief Introduction to Machine Learning for Engineers
By Osvaldo Simeone. 2017
158 citations recorded by the source corpus.
This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learning. The treatment concentrates on probabilistic models for supervised and unsupervised learning problems. It introduces fundamental concepts and algorithms by building on first principles, while also exposing the reader to more advanced topics with extensive pointers to the literature, within a unified notation and mathematical framework. The material is organized according to clearly defined categories, such as discriminative and generative models, frequentist and Bayesian approaches, exact and approximate inference, as well as directed and undirected models. This monograph is meant as an entry point for researchers with a background in probability and linear algebra.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- M. Di Renzo
- Yuxi Li
- Merouane Debbah
- Alessio Zappone
- Paul Patras
- Osvaldo Simeone
- Hamed Haddadi
- Chaoyun Zhang
Citations in the corpus
- Wireless Network Intelligence at the Edge (2018, neutral): “Basics of ML, its NN architectures, and communication designs were recently overviewed in [26] and [27].”
- Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? (2019, background): “As far as communications are concerned, most previous surveys discuss general machine learning techniques [14], [17], [18], [38]–[40], without providing a dedicated analysis of deep learning.”
- Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “Simeone [25] An introduction to machine learning for engineers.”
- Deep Reinforcement Learning: An Overview (2017, background): “2 MORE BOOKS • theoretical RL books (Bertsekas, 2012; Bertsekas and Tsitsiklis, 1996; Szepesvári, 2010) • an operations research oriented RL book (Powell, 2011) • an edited RL book (Wiering and van Otterlo, 2012) • Markov decision processes (Puterman, 2005) • machine learning (Bishop, 2011; Hastie e”
- A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, methodology): “It is noted that, with multi-layer neural networks, the computation of the gradient∇θ ln p(tn |xn , θ) yields the standard backpropagation algorithm [7], [19].”