Data-Driven Modeling & Scientific Computation

Lecture 1

 

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INTRODUCTION TO CLUSTERING AND CLASSIFICATION: This lecture provides an overview of the basic concepts behind supervised and unsupervised learning algorithms. Included is a discussion of k-means and knn (k-nearest neighbors).

 

MATLAB COMMANDS

SVD PCA KMEANS KNNSEARCH

 

Lecture 2

 

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[ Part 1 ][ 2 ][ 3 ][ 4 ]

 
ADVANCED CLUSTERING AND CLASSIFICATION: Classification algorithms such as Gaussian mixture models, Naive Bayes and Boosting are considered in these examples. Training and cross-validation are also considered.

 

MATLAB COMMANDS

FITGMDIST CLUSTER FITNAIVEBAYES NB.PREDICT CLASSIFY

 

Lecture 3

 

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[ Part 1 ][ 2 ][ 3 ]

 
SUPPORT VECTOR MACHINES AND CLASSIFICATION TREES: The classification schemes of support vector machines and regression tress are considered and implemented in MATLAB.

 

MATLAB COMMANDS

SVMTRAIN SVMCLASSIFY TREEFIT TREEVAL TREEDISP

 

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