Departments' graduate courses

Course start and periodicity may vary. Please see details for each course for up-to-date information. The courses are managed and administered by the respective departments. For more information about the courses, how to sign up, and other practical issues, please contact the examiner or course contact to be found in the course information.

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: Tue 22 Aug 2017.