To extract information from data automatically, by computational and statistical methods.

- Linear Methods for Regression
- Linear Methods for Classification
- Linear Discriminant Analysis
- Logistic Regression
- Separating Hyperplanes

- Basis Expansions and Regularization
- Kernel Methods
- Model Assessment and Selection
- Model Inference and Averaging
- Boostrapping
- EM Algorithm
- MCMC for Sampling fromthe Posterior
- Bagging
- Model Averaging and Stacking
- Stochastic Search: Bumping

- Additive Models, Trees, and Related Methods
- Generalized Additive Models
- Tree-Based Methods
- MARS: Multivariate Adaptive Regression Splines

- Boosting and Additive Trees
- Neural Networks
- Support Vector Machines and Flexible Discriminants
- Prototype Methods and Nearest-Neighbors
- Unsupervised Learning
- Association Rules
- Cluster Analysis
- Principal Components, Curves and Surfaces
- Independent Component Analysis and Exploratory Projection Pursuit
- Multidimensional Scaling

Sure you'll immediately know this table of contents comes from the book "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. However, only a limited part of topics will be covered here, as I'm not an expert on machine learning and not all of the topics are appropriate for animations.