Japanese

Academic Year	2026Year	School/Graduate School	Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program
Lecture Code	WSN23401	Subject Classification	Specialized Education
Subject Name	Statistical Machine Learning
Subject Name （Katakana）	スタティスティカルマシーンラーニング
Subject Name in English	Statistical Machine Learning
Instructor	ANDRADE SILVA DANIEL GEORG
Instructor (Katakana)	アンドラーデ　シルバ　ダニエル　ゲオルグ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year,  Second Semester,  4Term
Days, Periods, and Classrooms	(4T) Tues3-4,Thur5-6
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face
Credits	2.0	Class Hours/Week	4	Language of Instruction	E : English
Course Level	6 : Graduate Advanced
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	This lecture covers the statistical theory underpinning supervised learning (regression and classification). We cover basic concepts of statistical learning, like the bias-variance trade-off, and practical aspects of model evaluation and hyper-parameter selection. Starting from basic linear models, we proceed to neural networks and its implementation and training using PyTorch. The lecture consists of three types: ordinary lectures, hands-on sessions, and presentation by students.
Class Schedule	lesson1 Overview of Statistical Learning lesson2 Introduction to PyTorch lesson3 Linear Regression lesson4 Generalized Linear Models lesson5 Bias-Variance Trade-off lesson6 Penalized Likelihood Methods lesson7 Model Selection lesson8 High-Dimensional Data lesson9 Introduction to Deep Learning lesson10 Training of Neural Networks lesson11 Convolutional Neural Networks lesson12 Recurrent Neural Networks lesson13 Introduction to Bayesian Statistics lesson14 Markov Chain Monte Carlo Methods lesson15 Examination (Theory and Praxis)
Text/Reference Books,etc.	"An Introduction to Statistical Learning", Gareth James et al., Springer, 2021 "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" (Second Edition),  Trevor Hastie et al., Springer, 2016 "Deep learning: Foundations and concepts", Bishop, Christopher M., and Hugh Bishop, Springer Nature, 2023.
PC or AV used in Class,etc.	moodle
	(More Details)
Learning techniques to be incorporated	PBL (Problem-based Learning)/ TBL (Team-based Learning), Flip Teaching
Suggestions on Preparation and Review	Linear Algebra, Maximum Likelihood Method, Expectation and Variance of Estimator, Bayes Theorem, Python
Requirements	Basic knowledge in statistics, probability theory, linear algebra and python are a must.
Grading Method	Report (roughly 33%), Presentation (roughly 33%), Exam (roughly 33%)
Practical Experience	Experienced
Summary of Practical Experience and Class Contents based on it	Theoretical and Applied Research in Industry
Message
Other
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