Contents:
Chapter 14: Unsupervised Learning
- Introduction
- Learning without labels..
- Association Rules
- Market Basket Analysis
- The Apriori Algorithm
- Unsupervised as Supervised Learning
- Data Pooling
- Generalized Association Rules
- Cluster Analysis
- Priority Matrices
- Dissimilarities Based on Attributes
- Object Dissimilarity
- Clustering Algorithms
- Combinatorial Algorithms
- K-means
- Gaussian Mixtures as Soft K-Means Clustering
- Vector Quantization
- K-medoids
- Hierarchical Clustering
- Self-Organizing Maps
- Principal Components, Curves and Surfaces
- Principal Components
- Principal Curves and Surfaces
- Spectral Clustering
- Kernel Principal Components
- Sparse Principal Components
- Non-Negative Matrix Factorization
- Independent Component Analysis and Exploratory Projection Pursuit
- Nonlinear Dimension Reduction and Multidimensional Scaling
- The Google PageRank Algorithm
Chapter 15: Random Forests
- Definition of Random Forests
- Details of Random Forests
Chapter 16: Ensemble Learning
- Boosting and Regularization Paths
- Learning Ensembles
Chapter 17: Undirected Graphical Models
- Markov Graphs and Their Properties
- Undirected Graphical Models for Continous Variables
- Undirected Graphical Models for Discrete Variables
Chapter 18: High Dimensional Problems: p >> N
- Diagonal Linear Discriminant Analysis and Nearest Shrunken Centroids
- Linear Classifiers and Quadratic Regularization
- Regularized Discriminant Analysis
- Logistic Regression with Quadratic Regularization
- The Support Vector Classifier
- Feature Selection
- Computational Shortcuts When p >> N
- Linear Classifiers with {L_1} Regularization
- Classification when Features are Unavailable
- High-Dimensional Regression: Supervised Principal Components
- Feature Assessment and the Multiple Testing Problem
Comments
comments powered by Disqus