Pattern Recognition (PR) as an information reduction/mapping/characterization process. Introduction to statistical PR techniques. Feature vectors/spaces, discriminant functions, maximum likelihood test, Bayes decision theory, parameter estimation, parametric approaches to learning, non-parametric approaches to learning, K-nearest neighbor approach, serial decision methods. Unsupervised clustering, K-Means algorithm. Feature selection and extraction. Relaxation labeling, classification using Markov fields. Object description using the KLT transform (object eigenspaces), recognition using projective invariants. The course includes an extended programming project based on a recent scientific publication.
Learning Outcomes
Knowledge: Having attended and succeeded in the course, the student is able to describe how specific, selected pattern recognition problems are treated in recent, relevant literature. Understanding: Having attended and succeeded in the course, the student possesses in-depth understanding of the techniques and mechanisms for solving basic and more advanced pattern recognition problems and is able to justify why the employed mechanisms work for the task at hand. Application: Having attended and succeeded in the course, the student is able to reuse and deploy existing techniques to address novel problems and tasks in pattern recognition and also tackle specific domain applications. Analysis: Having attended and succeeded in the course, the student is able to formulate critical views of pattern recognition problems and decompose them in a series of individual subproblems. Synthesis: Having attended and succeeded in the course, the student is able to integrate solutions to individual subproblems in order to address more complex pattern recognition problems. Evaluation: Having attended and succeeded in the course, the student is able to assess and quantify the solutions to pattern recognition problems and compare them against existing competitive ones.
Student Performance Evaluation
Specific details on grading can be found on the course’ s website
The courses of the Computer Science Department are designated with the letters "CS" followed by three decimal digits. The first digit denotes the year of study during which students are expected to enroll in the course; the second digit denotes the area of computer science to which the course belongs.
First Digit
Advised Year of Enrollment
1,2,3,4
First, Second, Third and Fourth year
5,6
Graduate courses
7,8,9
Specialized topics
Second Digit
Computer Science Area
0
Introductory - General
1
Background (Mathematics, Physics)
2
Hardware Systems
3
Networks and Telecommunication
4,5
Software Systems
6
Information Systems
7
Computer Vision and Robotics
8
Algorithms and Theory of Computation
9
Special Projects
The following pages contain tables (one for each course category) summarizing courses offered by the undergraduate studies program of the Computer Science Department at the University of Crete. Courses with code-names beginning with "MATH" or "PHYS" are taught by the Mathematics Department and Physics Department respectively at the University of Crete.
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