Graduate Education Institute · Computer Science and Engineering · Master
Course Objective
The focus of this course is on the theory and application of pattern recognition techniques. Topics covered include machine pattern classification, feature extraction, object recognition, Bayesian decision theory, parametric and non-parametric pattern recognition, supervised and unsupervised pattern recognition, and an overview of these topics is provided.
Course Content
Learning and adaptation, Bayesian decision theory, discriminant functions, parametric techniques, maximum likelihood estimation, Bayesian estimation, adequate statistics, non-parametric techniques, linear discriminant functions, algorithm-independent machine learning, classifiers, unsupervised learning, grouping.
Required Resources
Duda, R.O.Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
Recommended Resources
Bishop, C. M. Pattern Recognition and Machine Learning. Springer. 2007; Marsland, S. Machine Learning: An Algorithmic Perspective. CRC Press. 2009. (Also uses Python.); Theodoridis, S. and Koutroumbas, K. Pattern Recognition. Edition 4. Academic Press, 2008.
Core Area Distribution
Teaching Methods
Assessment & Evaluation
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 16 | 3 | 48 |
| Out of Class Study Period | 0 | 0 | 0 |
| Midterm | 1 | 2 | 2 |
| Quiz | 0 | 0 | 0 |
| Assignment | 5 | 7 | 35 |
| Practice | 0 | 0 | 0 |
| Final | 1 | 2 | 2 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Introduction to Pattern Recognition, Learning and Adoption | lecture notes |
| 2 | Bayes Decision Theory | Lecture notes |
| 3 | Separator Functions | Lecture notes |
| 4 | Parametric Techniques: Maximum Likelihood Estimation and Bayesian Estimation, Sufficient Statistics | Lecture notes |
| 5 | Non-Parametric Techniques | Lecture notes |
| 6 | Linear Separator Functions | lecture notes |
| 7 | Linear Separator Functions | lecture notes |
| 8 | Midterm exam | Midterm exam |
| 9 | Non-Metric Methods | lecture notes |
| 10 | Algorithm-Independent Auto-Learning | Lecture notes |
| 11 | Algorithm-Independent Auto-Learning – Resampling | lecture notes |
| 12 | Algorithm-Independent Machine Learning – Classifiers | lecture notes |
| 13 | Unsupervised Learning and Grouping | lecture notes |
| 14 | Unsupervised Learning and Clustering | lecture notes |
| 15 | Unsupervised Learning and Clustering | lecture notes |
| 16 | Final | Final |