FEO3270 Pattern Classification and Machine Learning 8.0 credits

After an initial review of probabilistic models for multivariate data and the principles of Bayesian learning as opposed to point estimates of model parameters, these models and methods are further developed for various applications in regression and classification. The following main topics are covered:

• Generalized linear models for regression and classification

• Neural networks

• Kernel methods, especially sparse approaches, such as the Relevance Vector Machine (RVM) and Support Vector Machine (SVM)

• Graphical models, incl. Bayesian networks and Markov random fields

• Mixture models and Expectation Maximization

• Approximate inference methods, e.g., variational inference with factorized approximation

• Monte Carlo sampling methods

• Probabilistic (Bayesian) principal component analysis (PCA)

• Models for sequencial data, especially Hidden Markov models

After passing this course the student should be able to

• describe the general principles of probabilistic pattern classification and Bayesian parameter estimation,

• analyze previously unsolved problems in data classification or regression, for example problems encountered in the student's own research project, and formulate a theoretical probabilistic model,

• apply Bayesian parameter estimation, by selecting, if necessary, a suitable approximation approach to make the problem computationally tractable,

• understand and critially analyze new probabilistic pattern-recognition and machine-learning methods proposed in the scientific literature by other researchers

Mainly for PhD students in Electrical Engineering or Computer Science.

It requires solid background in probability theory. The undergraduate course EN2202 Pattern Recognition is a recommended but not compulsory prerequisite.

Bishop, C.M (2006). Pattern recognition and machine learning. Springer.

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

If the course is discontinued, students may request to be examined during the following two academic years.

Examination is based on active participation in course seminars and a final written individual open-book exam.

Active participation is required in at least 70% of course meetings. Individual 72-hour open-book exam with given advanced problems, of which at least 50% must be correctly solved.

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.