Clustering (Expectation/Maximization, Gaussian Mixture Models); Active learning; Online learning; Dimensionality reduction (PCA, LDA, Spectral clustering); Kernel methods; Deep learning (Convolutional Neural Networks); Sequence learning (Hidden Markov Models, Recurrent networks)
SU Credits : 3.000
ECTS Credit : 10.000
Prerequisite :
( Undergraduate level CS 412 Minimum Grade of D )
OR ( Masters Level CS 512 Minimum Grade of D )
OR ( Doctorate CS 512 Minimum Grade of D )
Corequisite :
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