Course Name: Statistical Learning Algorithms and Their Applications to Computer Vision
Credit: 2
Prerequisites: Pattern Recognition, stochastic process
Description:
The goal of this course is to introduce students to basic models and algorithms in machine learning. The contexts include the basic theory of statistics and Bayesian decision, some classical method for probability density estimation, mixture models and EM algorithm, preceptors and linear Discriminant function, statistical learning theory and support vector machine (SVM), combining models and boosting algorithm, random tree and random forest, model selection, introduction to graphical model, Bayesian network and Markov Random Field (MRF), belief propagation (BP) algorithm, graph cut algorithm, approximation inference and statistical computing, Hidden Markov Model (HMM) and condition random field (CRF), linear dynamic models for sequential data analysis.
