General description

Course organized by TUT, see TUT study guide for up-to-date information.

Learning outcomes

After completing the course, the student can:
- describe the basic structure of pattern recognition systems and the statistical bases of the classification theory (the Bayes classifier).
- distinguish supervised learning methods from the unsupervised ones.
- apply supervised learning methods (model-based maximum likelihood, k-nearest neighbours) to the classifier design.
- apply k-means clustering algorithm.

Contents

- The basic structure of pattern recognition systems. Supervised and unsupervised learning. Examples of pattern recognition systems.
- Basics of multivariate probability and statistics, class conditional density function, Bayesian decision theory, Bayes classifier
- Parametric (model-based) and nonparametric techniques (Parzen windows, k-nearest neighbours) for supervised learning.
- Linear classifiers and regression
- Validation of pattern recognition systems, cross-validation.
-Algorithms for unsupervised classification. K-means clustering.

Evaluation

2017–2018

Teaching

Archived Teaching Schedule. Please refer to current Teaching Shedule.

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