Understanding Machine Learning: From Theory to Algorithms

Download Understanding Machine Learning tutorial, a complete eBook created by Shai Shalev-Shwartz and Shai Ben-David.

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.

Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks.

Table of contents

  • Introduction
  • What Is Learning?
  • When Do We Need Machine Learning?
  • Types of Learning
  • Relations to Other Fields
  • How to Read This Book
  • Possible Course Plans Based on This Book
  • Notation
  • Part I Foundations
  • A Formal Learning Model
  • PAC Learning
  • A More General Learning Model
  • Releasing the Realizability Assumption – Agnostic PAC
  • Learning
  • The Scope of Learning Problems Modeled
  • Summary
  • Bibliographic Remarks
  • Exercises
  • Learning via Uniform Convergence
  • Uniform Convergence Is Sufficient for Learnability
  • Examples
  • Threshold Functions
  • Intervals
  • Axis Aligned Rectangles
  • Finite Classes
  • VC-Dimension and the Number of Parameters
  • The Fundamental Theorem of PAC learning
  • Proof of Theorem
  • Characterizing Nonuniform Learnability
  • Structural Risk Minimization
  • Minimum Description Length and Occam’s Razor
  • Occam’s Razor
  • Other Notions of Learnability – Consistency
  • Discussing the Different Notions of Learnability
  • The No-Free-Lunch Theorem Revisited
  • Summary
  • Bibliographic Remarks
  • Exercises
  • The Runtime of Learning
  • Learning -Term DNF
  • Efficiently Learnable, but Not by a Proper ERM
  • Hardness of Learning*
  • Bibliographic Remarks
  • Exercises
  • Part II From Theory to Algorithms
  • Linear Regression
  • Least Squares
  • Linear Regression for Polynomial Regression Tasks
  • Logistic Regression
  • Bibliographic Remarks
  • Exercises
  • The VC-Dimension of L(B, T)
  • AdaBoost for Face Recognition
  • Bibliographic Re
  • xii Contents
  • k-Fold Cross Validation
  • Train-Validation-Test Split
  • What to Do If Learning Fails
  • Bibliographic Remarks
  • Exercises
  • Controlling the Fitting-Stability Tradeoff
  • Bibliographic Remarks
  • Exercises
  • Stochastic Gradient Descent
  • Gradient Descent
  • Analysis of GD for Convex-Lipschitz Functions
  • Stochastic Gradient Descent (SGD)
  • Analysis of SGD for Convex-Lipschitz-Bounded Functions
  • Learning with SGD
  • SGD for Risk Minimization
  • Analyzing SGD for Convex-Smooth Learning Problems
  • SGD for Regularized Loss Minimization
  • Bibliographic Remarks
  • Exercises
  • Support Vector Machines
  • Margin and Hard-SVM
  • Bibliographic Remarks
  • Exercises
  • Kernel Methods
  • Embeddings into Feature Spaces
  • The Kernel Trick
  • Kernels as a Way to Express Prior Knowledge
  • Characterizing Kernel Functions*
  • Implementing Soft-SVM with Kernels
  • Summary
  • Bibliographic Remarks
  • Exercises
  • Multiclass, Ranking, and Complex Prediction Problems
  • One-versus-All and All-Pairs
  • Linear Predictors for Ranking
  • Bipartite Ranking and Multivariate Performance Measures
  • Linear Predictors for Bipartite Ranking
  • Bibliographic Remarks
  • Exercises
  • Nearest Neighbor
  • Analysis
  • A Generalization Bound for the -NN Rule
  • Feedforward Neural Networks
  • Learning Neural Networks
  • The Expressive Power of Neural Networks
  • Geometric Intuition
  • The Sample Complexity of Neural Networks
  • The Runtime of Learning Neural Networks
  • SGD and Backpropagation
  • Contents xv
  • Online Learnability
  • Online Classification in the Unrealizable Case
  • Weighted-Majority
  • Online Convex Optimization
  • The Online Perceptron Algorithm
  • Summary
  • Bibliographic Remarks
  • The k-Means Algorithm
  • Spectral Clustering
  • Graph Cut
  • Graph Laplacian and Relaxed Graph Cuts
  • Unnormalized Spectral Clustering
  • Information Bottleneck*
  • Maximum Likelihood Estimation for Continuous Random Variables
  • Maximum Likelihood and Empirical Risk Minimization
  • Linear Discriminant Analysis
  • Latent Variables and the EM Algorithm

 

Size : 2540.539 Kb
File type : pdf
Downloads: 280
Created: 2019-05-01
Understanding Machine Learning: From Theory to Algorithms

Others Machine Learning Tutorials

Understanding Machine Learning

An Introduction to Machine Learning, 2nd Edition

A Brief Introduction to Machine Learning for Engineers

An Introduction to Machine Learning

Machine Learning Yearning

Others related eBooks about Understanding Machine Learning: From Theory to Algorithms

Matters Computational: Ideas, Algorithms, Source code

This book provides algorithms and ideas for computationalists, whether a working programmer or anyone interested in methods of computation. The focus is on material that does not usually appear in textbooks on algorithms. ...

Computational and Inferential Thinking: The Foundations of Data Science

Data Science is about drawing useful conclusions from large and diverse data sets through exploration, prediction, and inference. Our primary tools for exploration are visualizations and descriptive statistics, for prediction are machine learning and optimization, and for inference are statistical t...

Wireless Hacking tutorial

Download free PDF tutorial about Wireless Hacking and Technic of protection, training document under 70 pages intended to beginners by Edri Guy....

Rethinking Productivity in Software Engineering

Get the most out of this foundational reference and improve the productivity of your softw..., download free Software Engineering tutorial in PDF (310 pages) created by Caitlin Sadowski ....

Blender 3D: Noob to Pro

This book is a series of tutorials to help new users learn Blender. The tutorials increase in difficulty, and later tutorials are built on the previous ones. Therefore, Blender beginners should follow the tutorials in sequence. Intermediate users can skip to a tutorial of suitable difficulty. Effo...

20 Awesome Uses for a Raspberry Pi

This is a free Raspberry PI PDF tutorial in 22 chapters and 21 pages. This document aims to give students 20 awesome projects that you can use Raspberry PI....

TouchDevelop, 3rd Edition

This book walks you through all of the screens of the TouchDevelop app, and itpoints out s..., download free TouchDevelop tutorial in PDF (270 pages) created by R. Nigel Horspool ....

Microservices AntiPatterns and Pitfalls

Remember when service-oriented architecture (SOA) was all the rage? Companies jumped in before fully understanding SOA's advantages and disadvantages, and struggled to make this complex architecture work. Today, we're poised to repeat this same experience with microservices - only this time we’r...

Docker for Developers

This book introduces the use of Docker focusing on best usage practices, based on the 12fa..., download free Docker tutorial in PDF (150 pages) created by Rafael Gomes ....

Introductionto the Assembly Language

This tutorial represente a brief introduction to assembly programming ,training courses in PDF under 77 pages designated to beginners....