APPLICABLE DEGREE PROGRAMS:
|Lecturer/Manager:||Professor Shih-Fu Chang|
|Office Hours:||Mondays 2:30-3:30pm, CEPSR 709|
|Office Phone:||(212) 854-6894|
|Email Address:||[email protected]|
|Day & Time Class
Meets on Campus:
|Monday/Wednesday, 1:10-2:25 PM|
|Credits for Course:||3|
Probability, Linear Algebra, or equivalent required
Familiarity with Digital Image Processing
Intended for beginning graduate students.
Introduction to theories, algorithms, and practical solutions of statistical pattern recognition. Topics covered include feature extraction, feature selection, Bayesian classifiers, neural networks, discriminative classifiers, clustering, performance evaluation, and fusion of models.
Students will gain understanding of algorithm design, mathematical tools, and practical implementations of various applications, with special emphasis on image classification and multimedia indexing. Benchmark datasets, such as TRECVID news video, and evaluation metrics will be used for course projects.
|Homework(s):||about 4 homeworks covering analytical questions and mini programming in language of your choice. Use of Matlab is suggested.|
|Midterm Exam(s):||One, open book|
|Final Exam:||One, open book|
|Grading:||4 homeworks (30%), midterm (25%), final (25%), one project (20%)|
|PC or notebook computer with access to Columbia’s systems|
Matlab is the recommended tool for the class. Software examples will be shown in class. Several public-domain tool libraries are available for running experiments.
Matlab will be installed in the computer lab located in Mudd Rm. 251. Most students also find it convenient to purchase a student edition of Matlab for their own computers. However, students may choose any language of his/her choice for homework submission.
All sample program and test data will be distributed on the course web site. There will be an online bulletin board for the class to exchange information and discuss common issues. Students need to have Columbia account in order to access the bulletin board.