Generic and Transferable Skills (GTS courses)
PhD courses given by the department
Pattern Classification and Machine Learning
- Course code: FSSY070
- Course higher education credits: 8.0
- Department: ELECTRICAL ENGINEERING
- Graduate school: Signals and Systems
- Course is normally given: The course will be given in the spring 2018.
- Language: The course will be given in English
Introduction: outline, "road map" for classification methods
Supervised learning methods
- Signal characterization, discriminative features, feature normalization, examples.
Unsupervised learning methods
- ML, Bayesian parameter estimation for pattern classification;
- Statistical learning and boosting methods (Support Vector Machines and Adaboost);
- Bayesian and belief networks;
- GMMs and EM algorithm.
Automated learning of features
- ML parameter estimation for component densities from the mixture density;
- Clustering methods (K-means, mean-shift);
- Topic models and BoW (bag of words);
- Richard O. Duda, Peter E. Hart, David G. Stork, Pattern classification, John Wiley & Sons, 2nd Ed.
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer.
- David Barber, Bayesian reasoning and machine learning, Cambridge university press, 2012.
- Related state-of-the-art publications and tutorial materials
- Matlab toolbox for PR.
Home exercises and Exam:
- Solving several theoretical problems
- Matlab programs on several selected topics and presentations
- Written exam: assignment of problem solving and programming tasks.