Pattern Recognition and Machine Learning

Contents

Introduction to PRML; General Notions: Parameter estimation, overfitting, model selection, curse of dimensionality, bias-variance tradeoff; Supervised Learning (Regression & Classification): Density estimation, Bayes decision theory, generative vs. discriminative models, Linear Methods: linear & logistic regression, generalized linear models, linear discriminant functions for classification, support vector machines etc., Nonlinear methods: kernel methods, nearest neighbor, \\ neural networks etc., Unsupervised Learning (Clustering & Density Estimations): K-means clustering, vector quantization, Gaussian mixture models, autoencoders, dimensionality reduction (linear & nonlinear) Handling Sequential Data: Hidden Markov models, and Linear Dynamical systems.