Lectures: Hisashi Kashima and Makoto Yamada
This course will cover in a broad sense the fundamental theoretical aspects and applicative possibilities of statistical machine learning, which is now a fundamental block of statistical data analysis and data mining. This course will focus first on the supervised and unsupervised learning problems, including a survey of probably approximately correct learning, Bayesian learning as well as other learning theory frameworks. Following this introduction, several probabilistic models and prediction algorithms, such as the logistic regression, perceptron, and support vector machine will be introduced. Advanced topic such as online learning, structured prediction, and sparse modeling will be also introduced.
Supervised learning and unsupervised learning
Linear and non-linear regression
Support vector machine and logistic regression
Learning theory
On-line learning
Model evaluation
Sparse modeling
Advanced topics (Semi-supervised learning, active learning, and structured output prediction)