Pattern Recognition is an important topic and finds applications in diverse areas such as healthcare, education, agriculture, environment, and transportation. It is closely associated with topics like machine learning and data mining.
The primary aim of this course is to attract the reader towards pattern recognition and provide a platform for understanding the basics and gaining an insight into several important topics associated with machine recognition. As a course taught for the first-year graduates, this class deals with the fundamentals of characterizing and recognizing patterns and features of interest of numerical data.
Please click "pr2013-test" to download.
Please refer to http://sse.tongji.edu.cn/liangshuang/pr2012fall/#schedule.
R. O. Duda, P. E. Hart, D. G. Stork,
2nd edition, John Wiley & Sons, Inc., 2000.
"The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics."
S.Theodoridis, K. Koutroumbas,
3rd edition, Academic Press, 2006.
"This book considers classical and current theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering and computer science.The authors have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information."