Understanding Machine Learning

Download free course Understanding Machine Learning, pdf file on 449 pages by Shai Shalev-Shwartz, Shai Ben-David.

The subject of this book is automated learning, or, as we will more often call it, Machine Learning (ML). That is, we wish to program computers so that they can "learn" from input available to them. Roughly speaking, learning is the process of converting experience into expertise or knowledge. The input to a learning algorithm is training data, representing experience, and the output is some expertise, which usually takes the form of another computer program that can perform some task. Seeking a formal-mathematical understanding of this concept, we'll have to be more explicit about what we mean by each of the involved terms: What is the training data our programs will access? How can the process of learning be automated? How can we evaluate the success of such a process (namely, the quality of the output of a learning program)?

Table of contents

Introduction
Foundations
A Gentle Start
A Formal Learning Model
Learning via Uniform Convergence
The Bias-Complexity Tradeoff
The VC-Dimension
Nonuniform Learnability
The Runtime of Learning
From Theory to Algorithms
Linear Predictors
Boosting
Model Selection and Validation
Convex Learning Problems
Regularization and Stability
Stochastic Gradient Descent
Support Vector Machines
Kernel Methods
Multiclass, Ranking, and Complex Prediction Problems
Decision Trees
Nearest Neighbor
Neural Networks
Additional Learning Models
Online Learning
Clustering
Dimensionality Reduction
Generative Models
Feature Selection and Generation
Advanced Theory
Rademacher Complexities
Covering Numbers
Proof of the Fundamental Theorem of Learning Theory
Multiclass Learnability
Compression Bounds
PAC-Bayes
Technical Lemmas
Measure Concentration
Linear Algebra

Pages :	449
Size :	3.5 MB
Downloads:	107
Created:	2022-02-03
License:	For personal or educational use
Author(s):	Shai Shalev-Shwartz, Shai Ben-David

Warning: Trying to access array offset on false in /home/tutovnfz/public_html/amp/article-amp.php on line 263

Download file

Others related eBooks about Understanding Machine Learning

Automated Machine Learning

Download free course Automated Machine Learning, pdf file on 223 pages by by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.

Automated Machine Learning: Methods, Systems, Challenges

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems.

Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers

Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of ma

A Brief Introduction to Machine Learning for Engineers

This book aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference. The intended readership consists of electric

Reinforcement Learning: An Introduction, Second Edition

Reinforcement Learning (RL), one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard