Numbering Code	G-INF01 63178 LE10	Year/Term	2022 ・ First semester
Number of Credits	2	Course Type	Lecture
Target Year		Target Student
Language	English	Day/Period	Mon.1
Instructor name	KASHIMA HISASHI (Graduate School of Informatics Professor) YAMADA MAKOTO (Graduate School of Informatics Associate Professor) TAKEUCHI KOH (Graduate School of Informatics Assistant Professor)
Outline and Purpose of the Course	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 on the supervised and unsupervised learning problems, including theoretical foundations such as a survey of probably approximately correct learning as well as their Bayesian perspectives and other learning theory frameworks. 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, transfer learning, and sparse modeling will be also introduced.
Course Goals	Understanding basic concepts, problems, and techniques of statistical learning and some of the recent topics.
Schedule and Contents	1. Statistical Learning Theory 1-1. Introduction to classification & regression: historical perspective, separating hyperplanes and major algorithms 1-2. Probabilistic framework of classification and statistical learning theory: Learning Bounds, Vapnik-Chervonenkis theory 2. Supervised Learning 2-1 Models for Classification: Logistic Regression, Perceptron, Support Vector Machines 2-2 Regularization: Sparse Models (L1 regularization), Bayesian Interpretations 2-3 Model Selection: Performance Measures, Cross-Validation, and Other Information Criterion 3. Advanced topics 3-1 Online learning 3-2 Semi-supervised, Active, and Transfer Learning 3-3 Recent topics in machine learning
Evaluation Methods and Policy	Grading will be based on reports and final exam according to Article 7 of Graduate School of Informatics Academic Grading Regulations.
Course Requirements	None
Study outside of Class (preparation and review)	Basic knowledge about probability and statistics is required. Students are expected to prepare and review for each class.
References, etc.	The Elements of Statistical Learning, Hastie, Friedman, Tibshirani, (Springer) Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz and Shai Ben-David, (Cambridge University Press)