Pattern Recognition

School of Software Engineering, Tongji University, 2013 Fall

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.

Click to view the MARKS and the ANSWER of Test One.
  1. Due Date: November 12, 2013.
  2. For the programming works, please use Matlab.
  3. All the documents you hand in, including comments in the source codes, should be in English.
  4. Please prepare your answer document in the format of MS Word, and name your file as “studentID_yourName”. Then package it together with your source codes into “studentID_yourName.rar” (or a zip file).
  5. You will need to submit a digital version (the package above by email), as well as a paper copy (answer document only in class), to Mr. Long Zhao.
Long Zhao:
Room 316, JiShi Building

Please refer to

Pattern Classification

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."
Pattern Recognition

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."