Pattern Recognition
CMPE 544Instructor: Assoc. Prof. İnci Baytaş
Course Description
This course will introduce the fundamentals of statistical pattern recognition with examples from several application areas. Topics include Bayesian decision theory, maximum likelihood and Bayesian parameter estimation, non-parametric techniques, linear discriminant functions, tree methods, multilayer neural networks, bias and variance in regression and classification, resampling for estimating statistics, bagging, boosting, unsupervised learning and clustering.
Topics (official, summarized)
- Bayesian decision theory
- Parametric and non-parametric techniques
- Linear discriminant functions
- Tree methods
- Clustering and dimensionality reduction
- Neural networks (incl. intro to deep learning)