Data-Driven Modeling & Scientific Computation
Lecture 1: Ch. 17.1
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: Ch. 17.2
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
