Introduction to Pattern Recognition
Data is displayed for academic year: 2023./2024.
Lecturers
Course Description
Pattern recognition. Basic motivation. Pattern recognition model. Examples of pattern recognition systems. Relation: artificial intelligence ? pattern recognition. Feature extraction and selection. Linear and non-linear transformations. Feature coding. Linear decision functions. Non-linear decision functions. Learning procedures for decision functions. Statistical classification. Bayes classifier. Estimation of parameters.Non-numerical pattern recognition. Structural classification. Syntactic recognition. Stochastic Grammars and Languages. Cluster analysis. Examples of pattern recognition system design.
Study Programmes
University undergraduate
[FER3-EN] Computing - study
Elective Courses
(6. semester)
[FER3-EN] Electrical Engineering and Information Technology - study
Elective Courses
(6. semester)
Learning Outcomes
- understanding basic concepts of pattern recognition
- apply the knowledge in pattern recognition system design
- integrate and combine knowledge for obtaining the new solutions
- evaluate and assess usefulness of pattern recognition methods
Forms of Teaching
Lectures
Lectures followed by numerous solutions to problems
ExercisesExamples of numerical solutions of problems
Grading Method
Continuous Assessment | Exam | |||||
---|---|---|---|---|---|---|
Type | Threshold | Percent of Grade | Threshold | Percent of Grade | ||
2. Mid Term Exam: Written | 50 % | 50 % | 50 % | |||
Final Exam: Written | 50 % | 50 % |
Week by Week Schedule
- Basic pattern recognition system models and application examples
- Linear and nonlinear decision functions
- Linear and nonlinear decision functions
- Linear and nonlinear decision functions
- Perceptron (learning paradigms,Hebbian learning, competitive learning, Boltzmann learning)
- Feature extraction and coding
- Bayes decision rule for classification
- Midterm exam
- Bayes decision rule for classification
- Multivariate Gaussian Bayes model
- Linguistic approach to pattern recognition, stochastic grammar inference
- Linguistic approach to pattern recognition, stochastic grammar inference
- K-means algorithm
- Adaptive clustering algorithms (ISODATA)
- Final exam
Literature
For students
General
ID 210752
Summer semester
4 ECTS
L1 English Level
L1 e-Learning
45 Lectures
0 Seminar
0 Exercises
0 Laboratory exercises
0 Project laboratory
Grading System
89 Excellent
74 Very Good
61 Good
50 Sufficient