Course Overview

Adaptive techniques in processing, recognition and retrieval of media information will be discussed. Much weight will be put on (re-)assuring the fundamental knowledge and algorithms in machine learning and signal/image processing, that are essential for adaptive handling of media contents. In addition, up-to-date methods in the field will also be mentioned. (Lecture in English)

Learning Achievement

Understanding of recent techniques of pattern recognition and machine learning to deal with media, especially recognition and processing of images.

Competence

Knowledge Utilization Skills,International Skills,Research Skills,Expert Knowledge

Course prerequisites

Basic knowledge on Linear Algebra, Analysis, Probability and Statistics of undergraduate level. Understanding of basic signal processing would be a plus.

Grading Philosophy

Two assignments (which includes problems that require programming) and the final term paper will be evaluated.

Course schedule

Adaptive techniques in processing, recognition and retrieval of media information will be discussed. Much weight will be put on (re-)assuring the fundamental knowledge and algorithms in machine learning and signal/image processing, that are essential for adaptive handling of media contents. In addition, up-to-date methods in the field will also be mentioned.
Weeks 1-2
Introduction and reviews on math used in this course.
Weeks 3-7
Theories and techniques for adaptation, recognition and retrieval.
Basic Pattern Recognition and the Bayes Rule
Linear Discrimination and Adaptive Filters
Neural Networks
Support Vector Machines
Clustering
Nearest Neighbor and
Weeks 8-10
Applications
Content-Based Image Retrieval (CBIR)
Biometric Authentication

Course type

Lectures

Online Course Requirement

Instructor

Kameyama Keisuke

Other information

Lecture will be given in English. Submissions must be made in English.

Site for Inquiry

Link to the syllabus provided by the university