Introduction to Machine Learning

Livro digital

Título:
Introduction to Machine Learning

Autor:
Amnon Shashua

Categoria:
Tecnologia > IA

Tags:
Machine Learning

Pessoa doadora:
Raffaello D. N.

Sinopse:
Readers who need a mathematically grounded route into machine learning will find a compact sequence that begins with Bayesian decision theory and builds toward formal learning guarantees. The table of contents moves from maximum likelihood and maximum entropy through the EM algorithm, support vector machines, and spectral methods, keeping inference and classification connected throughout. The later chapters develop PCA, LDA, canonical correlation analysis, k-means and spectral clustering, then turn to the formal PAC learning model, VC dimension, and the double-sampling theorem. Worked arguments and examples cover Gaussian mixtures, kernel functions, probabilistic reasoning, and the relationship between sample size and learnability, while an appendix collects supporting material. This is advanced course material for readers comfortable with probability, calculus, linear algebra, and optimization. Its value lies in showing how practical algorithms emerge from statistical assumptions and how their limits can be stated precisely, giving students and practitioners a rigorous foundation for evaluating models rather than treating machine learning as a catalogue of techniques.