Statistical Learning Theory (2018), Graduate School of Informatics, Kyoto University
Lecturers: Hisashi Kashima and Makoto Yamada
Day, time, and room: Monday, 8:45-10:15 / Research bldg. 8, Lecture room 4

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.

[Topics] (subject to changes of topics)
- 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
- Advaced topics (semi-supervised learning, active learning, and structured output prediction)


[Lecture Slides]
1. Introduction to machine learning
2. Regression
3. Classification
4. Model evaluation and selection
5. Statistical learning theory
The materials for the latter half of this course are found at Prof. Yamada's website

[Homework]
1. Predictive modeling challenge


[References]
- Competition site for homeworks: University of Big Data
- Lecture slides for the previous years: 2014, 2015, 2016, 2017