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Yasin

The document is a comprehensive introduction to machine learning tailored for engineers, covering fundamental concepts, applications, and methodologies. It includes topics such as supervised learning, linear regression, probabilistic models, and various approaches like frequentist and Bayesian methods. The content is structured into sections that progressively build understanding, making it suitable for both beginners and those looking to deepen their knowledge in machine learning.

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19 views2 pages

Yasin

The document is a comprehensive introduction to machine learning tailored for engineers, covering fundamental concepts, applications, and methodologies. It includes topics such as supervised learning, linear regression, probabilistic models, and various approaches like frequentist and Bayesian methods. The content is structured into sections that progressively build understanding, making it suitable for both beginners and those looking to deepen their knowledge in machine learning.

Uploaded by

sabsebada
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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arXiv:1709.02840v3 [cs.

LG] 17 May 2018

A Brief Introduction to Machine


Learning for Engineers
(2018), “A Brief Introduction to Machine Learning for Engineers”, :
Vol. XX, No. XX, pp 1–231. DOI: XXX.

Osvaldo Simeone
Department of Informatics
King’s College London
osvaldo.simeone@kcl.ac.uk
Contents

I Basics 5

1 Introduction 6
1.1 What is Machine Learning? . . . . . . . . . . . . . . . . . 6
1.2 When to Use Machine Learning? . . . . . . . . . . . . . . 8
1.3 Goals and Outline . . . . . . . . . . . . . . . . . . . . . . 11

2 A Gentle Introduction through Linear Regression 15


2.1 Supervised Learning . . . . . . . . . . . . . . . . . . . . . 15
2.2 Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Frequentist Approach . . . . . . . . . . . . . . . . . . . . 19
2.4 Bayesian Approach . . . . . . . . . . . . . . . . . . . . . 36
2.5 Minimum Description Length (MDL)∗ . . . . . . . . . . . 42
2.6 Information-Theoretic Metrics . . . . . . . . . . . . . . . . 44
2.7 Interpretation and Causality∗ . . . . . . . . . . . . . . . . 47
2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3 Probabilistic Models for Learning 51


3.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.2 The Exponential Family . . . . . . . . . . . . . . . . . . . 53
3.3 Frequentist Learning . . . . . . . . . . . . . . . . . . . . . 59

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