IGD - Interaction, Graphics & Design Track

Master of Computer Science - IP Paris


Multimodal/Robotics/Learning

CSC_5IA05_TA Learning for Robotics
2,0 ECTS (21h) - Parcours 3A IA ENSTA
Mai Nguyen
P3 Monday 09/02-13/04 08:30-11:45 and Tuesday 17/02 Calendar
This course will present some basic learning algorithms for robots : reinforcement learning and imitation learning. Students will have the chance to implement and try some of these algorithms on simple examples. Very wide application fields in cognitive robotics and social robotics will be presented.
CSC_51054_EP Machine and Deep Learning
5,0 ECTS (36h) - IVA – Website
Michalis Vazirgiannis, Johannes Lutzeyer
P1 Monday 23/09-16/12 14:00-18:15 Calendar
PrerequisitePython programming, Basics of probability and statistics
Introductory level class on Machine Learning on generic data: Supervised Learning, Unsupervised Learning, Kernels, Neural Networks, Basics of Deep learning and Graph neural network.
X-INF581A Advanced Deep Learning
5,0 ECTS (36h) - IVA – Website
Vicky Kalogeiton, Michalis Vazirgiannis, Johannes Lutzeyer
P2 Tuesday 08/01-18/03 13:30-17:45
PrerequisiteCSC_51054_EP or equivalent.
This course introduces students to the advanced principles of Deep Learning, including mathematical foundations, architecture design and practical applications. This course is particularly relevant given the current state of the job market, where Deep Learning skills are in high demand in many sectors, including technology, finance, healthcare and entertainment. The class include the following topics: GAN, Attention and Trasnformers, Graph Neural Networks, Neural architect search, and various interdisciplinary applications: Large Pretrained Models, Vision & Language, Biological Applications, Time Series.
CSC_52081_EP Reinforcement Learning and Autonomous Agents
5,0 ECTS (36h) - IVA – Website
Jesse Read
P2 Wednesday 07/01-18/03 14:00-18:15
PrerequisiteX-INF554 or equivalent.
This course selects a number of advanced topics to explore in machine learning and autonomous agents, in particular: Probabilistic graphicsal models (Bayesian networks), Multi-output and structured-output prediction problems, deep-learning architectures, Methods of search and optimization, Sequential prediction and decision making, Reinforcement learning.