## Machine Learning

### Machine Learning: The Basics

Topics to review so you don't get weeded out.
* Supervised learning
* Unsupervised learning
* Semi-supervised learning
* Modeling business decisions usually uses supervised and unsupervised learning.
* Classification and regression are the most commonly seen machine learning models.

### Machine Learning: The Full Topics List

List of topics focusing on theoretical components: 

* Supervised Learning
	* VC Dimension
	* PAC Learning
	* Noise 
	* Regression
	* Model Selection
	* Dimensions

* Bayesian Decision Theory
	* Classification
	* Losses and Risks
	* Discriminant Functions
	* Utility Theory
	* Association Rules

* Parametric Models
	* Maximum Likelihood Estimation
	* Building Estimators
	* Bayes Estimator
	* Parametric Classification
	* Regression
	* Tuning Model Complexity
	* Model Selection

* Multivariate Methods
	* Multivariate Data
	* Parameter Estimation
	* Missing Values
	* Multivariate Normal
	* Multivariate Classificaiton
	* Tuning Complexity
	* Discrete Features
	* Multivariate Regression

* Dimensionality Reduction
	* Subset Selection
	* PCA
	* Factor Analysis
	* Multidimensional Scaling
	* LDA
	* Isomapl
	* Local Linear Embedding

* Clustering
	* Mixture Densities
	* k-Means
	* Expectation Maximization Algorithm
	* Mixtures of Latent Variables
	* Supervised Learning after Clustering
	* Hierarchical Clustering
	* Numbers of Clusters

* Nonparametric Methods
* Decision Trees
* Linear discrimination
* Miultilyaer Perceptrons
* Local Models
* Kernel Machines
* Bayesian Estimation
* Hidden Markov Models
* Graphical Models
* Combining Multiple Learners
* Reinforcement Learning
* Design and Analysis of Machine Learning Experiments




A long and full list of types of models under each sub-heading: 

* Regression: **Modeling relationship between variables, iteratively refined using an error measure.**
	* Linear Regression
	* Logistic Regression
	* OLS (Ordinary Least Squares) Regression
	* Stepwise Regression
	* MARS (Multivariate Adaptive Regression Splines)
	* LOESS (Locally Estimated Scatterplot Smoothing)

* Instance Based: **Build up database of data, compare new data to database; winner-take-all or memory-based learning.**
	* k-Nearest Neighbor
	* Learning Vector quantization
	* Self-Organizing Map
	* Localy Weighted Learning

* Regularization: **Extension made to other methods, penalizes model complexity, favors simpler and more generalizable models.**
	* Ridge Regression
	* LASSO (Least Absolute Shrinkage and Selection Operator)
	* Elastic Net
	* LARS (Least Angle Regression)

* Decision Tree: **Construct a model of decisions made on actual values of attributes in the data.**
	* Classification and Regression Tree
	* CHAID (Chi-Squared Automatic Interaction Detection)
	* Conditional Decision Trees

* Bayesian: **Methods explicitly applying Bayes' Theorem for classification and regression problems.**
	* Naive Bayes
	* Gaussian Naive Bayes
	* Multinomial Naive Bayes
	* Bayesian Network
	* BBN (Bayesian Belief Network)

* Clustering: **Centroid-based and hierarchical modeling approaches; groups of maximum commonality.**
	* k-Means
	* k-Medians
	* Expectation Maximization
	* Hierarchical Clustering

* Association Rule Algorithms: **Extract rules that best explain relationships between variables in data.**
	* Apriori algorithm
	* Eclat algorithm

* Neural Networks: **Inspired by structure and function of biological neural networks, used ofr regression and classification problems.**
	* Radial Basis Function Network (RBFN)
	* Perceptron
	* Back-Propagation
	* Hopfield Network

* Deep Learning: **Neural networks that exploit cheap and abundant computational power; semi-supervised, lots of data.**
	* Convolutional Neural Network (CNN)
	* Recurrent Neural Network (RNN)
	* Long-Short-Term Memory Network (LSTM)
	* Deep Boltzmann Machine (DBM)
	* Deep Belief Network (DBN)
	* Stacked Auto-Encoders

* Dimensionality Reduction: **Find inherent structure in data, in an unsupervised manner, to describe data using less information.**
	* PCA
	* t-SNE
	* PLS (Partial Least Squares Regression)
	* Sammon Mapping
	* Multidimensional Scaling
	* Projection Pursuit
	* Principal Component Regression
	* Partial Least Squares Discriminant Analysis
	* Mixture Discriminant Analysis
	* Quadratic Discriminant Analysis
	* Regularized Discriminant Analysis
	* Linear Discriminant Analysis

* Ensemble: **Models composed of multiple weaker models, independently trained, that provide a combined prediction.**
	* Random Forest
	* Gradient Boosting Machines (GBM)
	* Boosting
	* Bootstrapped Aggregation (Bagging)
	* AdaBoost
	* Stacked Generalization (Blending)
	* Gradient Boosted Regression Trees