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Optimization and Learning

• Introduction
  • Polynomial Curve Fitting
  • Probability Distributions
  • The Frequentist View
  • The Bayesian View
  • Bayes’ Estimate
  • The Curse of Dimensionality
  • Information Theory
• Linear Models for Regression
  • Least Squares
  • Subset Selection
  • Shrinkage Methods
  • Principal Components Regression
  • Partial Least Squares
  • Example: Prostate Cancer
  • Basis Functions
  • Bayesian Regression
• Linear Models for Classification
  • Discriminant Functions
  • LDA: Iris Data
  • Generative Models
  • Discriminative Models
  • Bayesian Logistic Regression
• Linear Programming
  • Lagrangian Relaxation
  • Farkas’ Lemma
  • Strong Duality
  • Algorithms
• Neural Networks
  • Keras
  • Feedfoward Neural Networks
  • Network Training
  • Regression
  • Classification
  • Regularization
• Convolutional Neural Networks
  • Introduction
  • Dogs vs. Cats
  • Visualizing the Convnets
  • Deep Dream
  • Neural Style Transfer
• Kernel Methods
  • Gaussian Process for Regression
• Sequential Data Models
  • Hidden Markov Models
  • Linear Dynamical System
• Text Mining
  • Word Embeddings
  • Recurrent Neural Networks
  • Regularization
  • Text Generation
• Conic Programming
  • Formulation
  • Duality
  • Applications
• Decomposition Methods
• Advanced Deep-Learning
  • Non-Sequential Models

Indices and tables

• Index

• Module Index

• Search Page