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
File type :	PDF
Downloads:	82
Created:	2022-02-03
License:	For personal or educational use
Author(s):	Shai Shalev-Shwartz, Shai Ben-David

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