AU: 4.0
Programme: CSC(CE)

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

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