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: 71
Created: 2022-02-03
License: For personal or educational use
Author(s): Shai Shalev-Shwartz, Shai Ben-David
Understanding Machine Learning

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