弗吉尼亚理工计算机学院研究生课程 Advanced Machine Learning
This course will cover the science of machine learning. It focuses on the mathematical foundations and analysis of machine learning methods and how they work.
课题包括：

• Overview of machine learning: learning from data; overfitting, regularization, cross-validation.
• Supervised learning: decision trees, naive Bayes, logistic regression, support vector machines, neural networks
• Unsupervised and semi-supervised learning: clustering (k-means, Gaussian mixtures); principal components analysis
• Learning theory: probably approximately correct (PAC) learning, model complexity, bias and variance
• Structured models: Bayesian networks, Markov random fields, hidden Markov models
• Deep learning: convolutional neural networks, recurrent neural networks
• Other topics: reinforcement learning, machine learning applications (vision, natural language processing, recommendation)