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Full Description
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 The answer is often "for a general, but slightly outdated introduction, read this book; for a detailed survey of methods based on probabilistic models, check this reference; to learn about statistical learning, this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.
Contents
I. Basics
1. Introduction
2. A Gentle Introduction through Linear Regression
3. Probabilistic Models for Learning
II. Supervised Learning
4. Classification
5. Statistical Learning Theory
III. Unsupervised Learning
6. Unsupervised Learning
IV. Advanced Modelling and Inference
7. Probabilistic Graphical Models
8. Approximate Inference and Learning
V. Conclusions
9. Concluding Remarks
Appendices
Acknowledgements
References
