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:

Introduction: outline, "road map" for classification methods

  • Signal characterization, discriminative features, feature normalization, examples.
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.
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);
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:
  1. Solving several theoretical problems
  2. Matlab programs on several selected topics and presentations
  3. Written exam: assignment of problem solving and programming tasks.
Irene Gu
More information
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Page manager Published: Fri 18 Dec 2020.