適応的メディア処理

適応的メディア処理_E
Instructor(s)	Keisuke Kameyama
E-Mail	Keisuke.Kameyama@cs.tsukuba.ac.jp
URL	https://adapt.cs.tsukuba.ac.jp/moodle342/course/view.php?id=2 (guest access allowed)
Office hours	Please contact by mail.
Cource#	01CH609, 01CF114
Area	Media Engineering
Basic/Advanced
Course style	Lecture
Term	Spring AB
Period	Mon2
Room#	3B303
Keywords	Pattern Recognition, Adaptation, Feature Extraction, Image Processing, Machine Learning
Prerequisites	Basic knowledge on Linear Algebra, Analysis, Probability and Statistics of undergraduate level. Understanding of basic signal processing would be a plus.
relation degree program competence	Knowledge Utilization Skills,International Skills,Research Skills,Expert Knowledge
Goal
Outline	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.
Course plan	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 Subspace Methods Weeks 8-10 Applications Content-Based Image Retrieval (CBIR) Biometric Authentication Classification of general object images
Textbook
References	C. Bishop, Neural networks for pattern recognition, Oxford Univ. Press 1995 Haykin, Neural networks - A comprehensive foundation - Prentice Hall 1998 F. M. Ham, I. Kostanic, Principles of neurocomputing for science and engineering, McGraw-Hill, 2001 C. Bishop, Pattern recognition and machine learning, Springer 2006 (邦訳あり) 熊沢逸夫、学習とニューラルネットワーク、森北出版．
Evaluation	Irregular small assignments and final term paper will be evaluated.
TF / TA	None
Misc.	Lecture in English