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PhD courses
Generic and Transferable Skills
Information about 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
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
E-mail: irenegu@chalmers.se
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