The goals of this unit include teaching the foundations and concepts associated to Bayesian models used in problems of image analysis, image segmentation and image classification.
Probability. Probability models. Modeling complex data densities. Regression models. Classification models. Graphical models.
Algebra, Analysis, Signals and Systems, Control, Computational Mathematics.
Generic skills to reach
. Competence in analysis and synthesis; . Knowledge of a foreign language; . Competence to solve problems; . Critical thinking; . Creativity; . Competence in organization and planning; . Competence for working in group; . Adaptability to new situations; . Quality concerns; . Research skills; (by decreasing order of importance)
Teaching hours per semester
total of teaching hours
assessment implementation in 20132014 Assessment Evaluation is either performed based on Matlab computational projects (100%) or Matlab computational projects (50%) and a final test (50%): 100.0%
Bibliography of reference
“Computer vision: models, learning and inference”, Simon Prince
“Bayesian Reasoning and Machine Learning”, David Barber
Teaching includes theoretical classes using slides and after the basis having been introduced seminars prepared by the students on topics previously agreed upon.