Title A Brief Introduction to Machine Learning for Engineers
Subtitle
Author Osvaldo Simeone
ISBN 9781680834727
List price USD 99.00
Price outside India Available on Request
Original price
Binding Paperback
No of pages 250
Book size 153 x 235 mm
Publishing year 2018
Original publisher Now Publishers (Eurospan Group)
Published in India by .
Exclusive distributors Viva Books Private Limited
Sales territory India, Sri Lanka, Bangladesh, Pakistan, Nepal, .
Status New Arrival
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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 The answer is often “for a general, but slightly outdated introduction, read this book; for a detailed survey of methods based on probabilistic models, check this reference; to learn about statistical learning, this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:

Part I. Basics

Chapter 1: Introduction • What is Machine Learning? • When to Use Machine Learning? • Goals and Outline

Chapter 2: A Gentle Introduction through Linear Regression • Supervised Learning • Inference • Frequentist Approach • Bayesian Approach • Minimum Description Length (MDL)* • Information-Theoretic Metrics • Interpretation and Causality* • Summary

Chapter 3: Probabilistic Models for Learning • Preliminaries • The Exponential Family • Frequentist Learning • Bayesian Learning • Supervised Learning via Generalized Linear Models (GLM) • Maximum Entropy Property* • Energy-based Models* • Some Advanced Topics* • Summary

 

Part II. Supervised Learning

Chapter 4: Classification • Preliminaries: Stochastic Gradient Descent • Classification as a Supervised Learning Problem • Discriminative Deterministic Models • Discriminative Probabilistic Models: Generalized Linear Models • Discriminative Probabilistic Models: Beyond GLM • Generative Probabilistic Models • Boosting* • Summary

Chapter 5: Statistical Learning Theory* • A Formal Framework for Supervised Learning • PAC Learnability and Sample Complexity • PAC Learnability for Finite Hypothesis Classes • VC Dimension and Fundamental Theorem of PAC Learning • Summary

 

Part III. Unsupervised Learning

Chapter 6: Unsupervised Learning • K-Means Clustering • ML, ELBO and EM • Directed Generative Models • Undirected Generative Models • Discriminative Models • Autoencoders • Ranking* • Summary

 

Part IV. Advanced Modelling and Inference

Chapter 7: Probabilistic Graphical Models • Introduction • Bayesian Networks • Markov Random Fields • Bayesian Inference in Probabilistic Graphical Models • Summary

Chapter 8: Approximate Inference and Learning • Monte Carlo Methods • Variational Inference • Monte Carlo-based Variational Inference* • Approximate Learning* • Summary

 

Part V. Conclusions

Chapter 9: Concluding Remarks

 

AppendicesAppendix A: Information Measures • Entropy • Conditional Entropy and Mutual Information • Divergence Measures • Appendix B: KL Divergence and Exponential Family • Acknowledgements • References


Target Audience:

This book is the entry point to machine learning for students, practitioners, and researchers with an engineering background in probability and linear algebra.

 

 
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