This course focuses on the principles of learning from data and quantification of uncertainty, by complementing and enriching the Introduction to Statistical Learning course. In particular, the course is divided into two main parts that correspond to the supervised and unsupervised learning paradigms. The presentation of the material follows a common thread based on the probabilistic data modeling approach, so that many classical algorithms, such as least squares and k-means, can be seen as special cases of inference problems for more general probabilistic models. Taking a probabilistic view also allows the course to derive inference algorithms for a class of nonparametric models that have close connections with neural networks and support vector machines. Similarly to the Introduction to Statistical Learning course, the focus is not on the algorithmic background of the methods, but rather on their mathematical and statistical foundations. This advanced course is complemented by lab sessions to guide students through the design and validation of the methods developed duringthe lectures.
Teaching and Learning Methods : Lectures and Lab sessions (preferably one student per group)
Course Policies : Attendance of Lab sessions is mandatory
This course covers the application-level protocols dedicated to IOT. Knowing the limited capacity, in terms of battery and CPU, of the things, the classical application protocols used in the Internet like HTTP are not adequate. This course presents the recent application protocols specially developed for IOT. These protocols are organized into two categories: (i) Client/server (like COAP) and (ii) Publish/Subscribe (like MQTT, XMPP). In addition to these protocols, this course introduces two types of architecture, specifically dedicated to host IOT services, like 3GPP MTC and oneM2M.
Teaching and Learning Methods: The course is organised in lectures and labs.
Course Policies: Labs are Mandatory (attendance + reports)
This course aims at providing a solid and practical algorithmic foundation to the design and use of scalable machine learning algorithms, with particular emphasis on the MapReduce programming model. Students will get familiar with a wide range of topics, through the application of theoretic ideas on problems of practical interest. This is a "reverse class", in which students are required to study (or revise) a particular topic at home, and apply what they have learned solving real world problems, including industrial applications, during numerous laboratory sessions. Laboratory sessions are based on modern technologies such as Jupyter Notebooks.
Teaching and Learning Methods: Laboratory sessions (group of 2 students)
Course Policies: Attendance to Lab sessions is mandatory.
This course introduces the main concepts and techniques used in computer graphics and image synthesis. It focuses on 3D object modelling and advanced visualization methods used in 3D and Virtual imaging, scientific and information visualization, CAD, flight simulation, games, advertising and movie special effects. The courses mixes theoretical and practical sessions and the project requires a student personal involvement.
Teaching and Learning Methods :Lectures, Lab sessions and project (groups of 2 to 4 students)
Course Policies : It is mandatory to
- Attend to all the Lab sessions
- Deliver of a project movie and attend the project contest.