大学院演習IIIB

博士後期課程理工学研究科 - 理工学専攻

DSCT1702

コース情報

担当教員: 矢入郁子

単位数: 1

年度: 2024

学期: 秋学期

曜限: 火1

形式: 対面授業

レベル: 800

アクティブラーニング: なし

他学部履修: 不可

評価方法

出席状況

50%

授業参加

50%

詳細情報

概要

This lecture will be done interactively using yairilab.net domain online server. You will learn how to perform three typical pattern classification methods: cluster analysis, support vector machines, and deep neural networks. This lecture is equivalent to the subject of “Cultivating specialized skills with combined knowledge” in the curriculum policy of the doctor's program at the Graduate School of Science and Engineering.

目標

The goal is to acquire skills that can apply deep neural networks to your research. This lecture corresponds to the Diploma Policy 2 of the doctor's program at the Graduate School of Science and Engineering. Students will acquire the expertise to be at the forefront in science and engineering and related fields, as well as the ability to develop new technologies and develop new fields.

授業外の学習

Take this lesson as an opportunity to deepen your knowledge and thoughts and increase your ability to execute. Thinking about the application of these technologies in your own research in some way every day, all of them are preparation and review of this class. As a guideline, a minimum of 1.5 hours of preparation / review is required.

所要時間: 190分

スケジュール

Doctor's Thesis Research and Pattern Classification Methods
Examples of pattern classification in information and communication research
An overview of pattern classification methods that ICT researchers should learn
Introduction to supervised Deep Leaning
Application of supervised Deep Learning to your research, basic
Applying supervised Deep Learning to your research, advanced
Applying supervised Deep Learning to your research, professional
Introduction to weak supervised Deep Learning
Application of weak supervised Deep Learning to your research, basic
Application of weak supervised Deep Learning to your research, advanced
Application of weak supervised Deep Learning to your research, professional
Introduction to no supervised Deep Learning
Application of no supervised Deep Learning to your research, basic
Application of no supervised Deep Learning to your research, advanced

教科書

All materials are prepared and provided to all students.

参考書

書籍情報はありません。