Statistical Learning Theory (2017), Graduate School of Informatics, Kyoto University
Hisashi Kashima
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.

yTopicsz
- 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)


yLecture Slidesz
1. Introduction to machine learning
2. Regression
3. Classification
4. Model evaluation
5. Ensemble methods
6. Sparsity
7. Semi-supervised, Active, and Transfer Learning
8. Statistical learning theory
9. On-line learning

yHomeworkz
1. Predictive modeling challenge
2. Tutorial (by Jiuding Duan)

yReferencesz
- Competition site for homeworks: University of Big Data
- Lecture slides for the previous years: 2014, 2015, 2016
- Past exam: 2016