Machine Learning

Catalog description

This course introduces machine learning concepts such as unsupervised and semi-supervised learning, naive bayesian, decision trees/regression tree, K-means, K-NN, regression, SVM, neural networks. The associated training & operating algorithms are presented, including ensemble machine learning. Solutions for related problems such as classification, regression, anomaly detection, time series prediction, image-to-image, sequence-to-sequence, rule learning, Markov chain learning etc. Concepts used to prepare training datasets.