筑波大学教育課程編成支援システム(EN)

02RB245 Basics of Machine Learning

2.0 Credits, 1 - 3 Year, SprAB Fri1,2
Masakazu Hirokawa

Course Overview

This course aims to facilitate students’ broad understanding of Machine Learning technologies, in particular classification algorithms, through coursework and exercises.

Remarks

Those who do not belong to the PhD program in Empowerment Informatics need the permission of the instructor to register.Student who registers the following courses: Fundamentals of Intelligent Interaction Systems Theory(01CK502), Tools for Intelligent Interaction Systems a(01CK916), cannot register this course.

Course Type

lectures and class e

Course Remarks

Those who do not belong to the PhD program in Empowerment Informatics need the permission of the instructor to register.
Student who registers the following courses: Fundamentals of Intelligent Interaction Systems Theory(01CK502), Tools for Intelligent Interaction Systems a(01CK916), cannot register this course.

We will support non-native speakers of Japanese language by explaining materials in English

Relationship to EMP Educational Objectives

Interdisciplinary ability:Broad specialist knowledge and experience

Course Objectives

This course aims to facilitate students’ broad understanding of Machine Learning technologies, in particular classification algorithms through coursework and exercises.

Keywords

Course Schedule

This course consists of the following contents:
- Overview of ML technologies
- Maximum a posteriori probability estimation (Naive Bayes)
- k-means method
- Particle filter
- Hidden Markov Model
- The curse of dimensionality
- Linear classification / Principle Component Analysis
- Subspace method / k-Nearest Neighbor method
- Support Vector Machine
- Convolutional Neural Network
- Application in image processing

Graduating Methods and Criteria

Completing exercises and submitting reports

Homework

Completing specified online coursework or auditing Fundamentals of Intelligent Interaction Systems Theory(01CK502) is required.

Textbook

1. Bishop, C.M.,Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
2. Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin,A Practical Guide to Support Vector Classication, https://www.csie.ntu.edu.tw/~cjlin/libsvm/
3. 岡谷貴之,深層学習、講談社 (2015)
4. OpenCV Tutorials (3.1.0-dev) http://docs.opencv.org/trunk/d9/df8/tutorial_root.html

シラバス参照