All students must validate 30 ECTS per semester.We offer two speciality tracks:
Track 1: Vision and Applications
The first track focuses on various applications of computer vision: biomedical applications, people detection, object tracking, computational photography.
Track 2: Vision and devices
The second track focuses on devices to capture images (intelligent sensors, medical imaging systems) and to visualise and interact with them (augmented reality)
The objective of this subject is to address machine-learning problems from a Bayesian perspective. Graphical models (GMs) will be introduced as probabilistic models in which dependence and independence relations between random variables are described in terms of a graph. Similarly, Bayesian networks are a particular case of GMs that are especially useful for modeling conditional independences. Exact inference algorithms will be addressed (such as variable elimination, sum-product and junction tree) and the way they can be applied efficiently. These will be studied in this course alongside with the relation between inference and learning. More general approximate inference methods, either deterministic (e.g. Variational inference or expectation propagation) or based on sampling and simulation (e.g. Monte Carlo methods based on Markov chains), will also be introduced in this course.
The European Credit Transfer and Accumulation System (ECTS) is a student-centred system based on the student workload required to achieve the objectives of a programme of study. Its aim is to facilitate the recognition of study periods undertaken abroad by mobile students through the transfer credits. The ECTS is based on the principle that 60 credits are equivalent to the workload of full-time student during one academic year.