PhD courses

​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
  • 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
Course content:
1. Introduction: outline, "road map" for classification methods
 Signal characterization, discriminative features, feature normalization, examples.
2. Supervised 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.
3. Unsupervised learning methods
 ML parameter estimation for component densities from the mixture density;
 Clustering methods (K-means, mean-shift);
 Topic models and BoW (bag of words);
4. Automated learning of features
 NNs and deep learning

Course book/material:
1. Richard O. Duda, Peter E. Hart, David G. Stork,  Pattern classification, John Wiley & Sons, 2nd Ed.
2. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer.
3. David Barber, Bayesian reasoning and machine learning, Cambridge university press, 2012.
4. Related state-of-the-art publications and tutorial materials
5. 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.

Irene Gu
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Irene Gu

Page manager Published: Fri 18 Dec 2020.