General description

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

Learning outcomes

Students understand principles of selected pattern recognition and machine learning approaches for interpreting signals. Student can apply the methods to real problems.

Contents

- Historical perspective to signal interpretation using pattern recognition and machine learning (concept learning, expert systems etc.) Practical application examples.
- Decision tree learning and random forests.
- Bayesian decision making and learning.
- Probability, decision and information theories in machine learning and pattern recognition.
- Probability distributions. Mixture models and EM.
- Linear models for regression and classification.
- Algorithm-independent machine learning. Evaluating hypothesis. No free lunch theorem, Occam's razor and cross-validation. High-dimensional problems. Feature selection.

Evaluation

Belongs to following study modules

2017–2018

Teaching

Archived Teaching Schedule. Please refer to current Teaching Shedule.

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