Understanding Machine Learning: From Theory to AlgorithmsCambridge University Press, 19 במאי 2014 - 397 עמודים 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. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering. |
תוכן
Foundations | 11 |
A Formal Learning Model | 22 |
Learning via Uniform Convergence | 31 |
The BiasComplexity Tradeoff | 36 |
The VCDimension | 43 |
Nonuniform Learnability | 58 |
The Runtime of Learning | 73 |
From Theory to Algorithms | 87 |
Decision Trees | 212 |
Nearest Neighbor | 219 |
Neural Networks | 228 |
Additional Learning Models | 243 |
Clustering | 264 |
Dimensionality Reduction | 278 |
Generative Models | 295 |
Feature Selection and Generation | 309 |
Boosting | 101 |
Model Selection and Validation | 114 |
Convex Learning Problems | 124 |
Regularization and Stability | 137 |
Stochastic Gradient Descent | 150 |
Support Vector Machines | 167 |
Kernel Methods | 179 |
Multiclass Ranking and Complex Prediction Problems | 190 |
Advanced Theory | 323 |
Covering Numbers | 337 |
Multiclass Learnability | 351 |
Compression Bounds | 359 |
Appendix A Technical Lemmas | 369 |
Linear Algebra | 380 |
| 395 | |
מהדורות אחרות - הצג הכל
מונחים וביטויים נפוצים
AdaBoost approximation error argmax argmin assume assumption binary classification Chapter class H classifier clustering compression computational concludes our proof convex function decision stumps decision tree define definition denote dimension dimensional distribution domain empirical risk Equation ERM rule estimation error example feature space function f given halfspaces hinge loss hypothesis class implement inequality input instances k-means kernel label LD(h LD(w learning algorithm learning problems Lemma Let H linear predictors loss function Lp(h Ls(h machine learning matrix minimal multiclass neural networks neuron No-Free-Lunch theorem norm obtain output overfitting PAC learnable PAC learning papaya parameter Perceptron polynomial prediction prior knowledge probability prove random variable regression S~Dm sample complexity sequence shattered solve Stochastic Gradient Descent subgradient task Theorem training set uniform convergence upper bound v₁ VC-dimension VCdim(H vector weak learner x₁