Machine Learning

Overview: introduction, definition, fundamental concepts, applications. Unsupervised learning: K-means, vector quantization, self-organizing neural networks. Supervised learning: K nearest neighbour, learning vector quantization, decision tree, supervised neural networks. Graphical models: belief networks, Bayesian networks, Hidden Markov models, incremental learning, reinforcement learning, machine learning applications