

@ 67,7 +67,7 @@ List of topics focusing on theoretical components: 







* Nonparametric Methods 



* Decision Trees 



* Lienar iscrimination 



* Linear discrimination 



* Miultilyaer Perceptrons 



* Local Models 



* Kernel Machines 


@ 76,7 +76,6 @@ List of topics focusing on theoretical components: 



* Graphical Models 



* Combining Multiple Learners 



* Reinforcement Learning 







* Design and Analysis of Machine Learning Experiments 










@ 84,8 +83,7 @@ List of topics focusing on theoretical components: 







A long and full list of types of models under each subheading: 







* Regression 



* **Modeling relationship between variables, iteratively refined using an error measure.** 



* Regression: **Modeling relationship between variables, iteratively refined using an error measure.** 



* Linear Regression 



* Logistic Regression 



* OLS (Ordinary Least Squares) Regression 


@ 93,55 +91,47 @@ A long and full list of types of models under each subheading: 



* MARS (Multivariate Adaptive Regression Splines) 



* LOESS (Locally Estimated Scatterplot Smoothing) 







* Instance Based 



* **Build up database of data, compare new data to database; winnertakeall or memorybased learning.** 



* Instance Based: **Build up database of data, compare new data to database; winnertakeall or memorybased learning.** 



* kNearest Neighbor 



* Learning Vector quantization 



* SelfOrganizing Map 



* Localy Weighted Learning 







* Regularization 



* **Extension made to other methods, penalizes model complexity, favors simpler and more generalizable models.** 



* 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.** 



* Decision Tree: **Construct a model of decisions made on actual values of attributes in the data.** 



* Classification and Regression Tree 



* CHAID (ChiSquared Automatic Interaction Detection) 



* Conditional Decision Trees 







* Bayesian 



* **Methods explicitly applying Bayes' Theorem for classification and regression problems.** 



* 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 



* **Centroidbased and hierarchical modeling approaches; groups of maximum commonality.** 



* Clustering: **Centroidbased and hierarchical modeling approaches; groups of maximum commonality.** 



* kMeans 



* kMedians 



* Expectation Maximization 



* Hierarchical Clustering 







* Association Rule Algorithms 



* **Extract rules that best explain relationships between variables in data.** 



* 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.** 



* Neural Networks: **Inspired by structure and function of biological neural networks, used ofr regression and classification problems.** 



* Radial Basis Function Network (RBFN) 



* Perceptron 



* BackPropagation 



* Hopfield Network 







* Deep Learning 



* **Neural networks that exploit cheap and abundant computational power; semisupervised, lots of data.** 



* Deep Learning: **Neural networks that exploit cheap and abundant computational power; semisupervised, lots of data.** 



* Convolutional Neural Network (CNN) 



* Recurrent Neural Network (RNN) 



* LongShortTerm Memory Network (LSTM) 


@ 149,8 +139,7 @@ A long and full list of types of models under each subheading: 



* Deep Belief Network (DBN) 



* Stacked AutoEncoders 







* Dimensionality Reduction 



* **Find inherent structure in data, in an unsupervised manner, to describe data using less information.** 



* Dimensionality Reduction: **Find inherent structure in data, in an unsupervised manner, to describe data using less information.** 



* PCA 



* tSNE 



* PLS (Partial Least Squares Regression) 


@ 164,8 +153,7 @@ A long and full list of types of models under each subheading: 



* Regularized Discriminant Analysis 



* Linear Discriminant Analysis 







* Ensemble 



* **Models composed of multiple weaker models, independently trained, that provide a combined prediction.** 



* Ensemble: **Models composed of multiple weaker models, independently trained, that provide a combined prediction.** 



* Random Forest 



* Gradient Boosting Machines (GBM) 



* Boosting 


@ 174,4 +162,3 @@ A long and full list of types of models under each subheading: 



* Stacked Generalization (Blending) 



* Gradient Boosted Regression Trees 










