Intelligent Systems

Course description

Artificial intelligence is about the construction and study of systems that can automatically learn from data. With the emergence of massive datasets commonly encountered today, the need for powerful machine learning is of acute importance. Examples of successful applications include effective web search, anti-spam software, computer vision, robotics, practical speech recognition, and a deeper understanding of the human genome.

This course gives an introduction to this exciting field. In the first part, we will introduce basic machine learning techniques. The second part will focus on symbolic AI and the third part will introduce Deep Learning methods.




Lectures are scheduled from 8:00 to 9:30 on Fridays (most in room H202 of Ensimag), and practical session from 9:45 to 11:15 (most in rooms E100 and E101 of Ensimag) (iCal).

# Date Room Teacher Topic
1 03/02/2023 H202 - E100 PG/MA Introduction to Artificial Intelligence (TP)
2 10/02/2023 H202 - E100/E101 PG/MA Unsupervised Learning (TP)
3 24/02/2023 H202 - E100/E101 PG/MA Supervised Learning (TP)
4 03/03/2023 H202 - E100/E101 PG/MA Regularization (TP)
5 10/03/2023 TBD MCLR Symbolic AI (1)
6 17/03/2023 H202 - E100/E101 MCLR Symbolic AI (2)
7 24/03/2023 H202 - E100/E101 MCLR Symbolic AI (3)
8 31/03/2023 TBD PG Deep Learning (1)
9 07/04/2023 H202 - E100/E101 PG/MA/JSF Deep Learning (2) (TP)
10 21/04/2023 H202 MA/JSF Deep Learning (TP)
11 28/04/2023 H202 PG/MCLR Revision
# Btw May 2 and May 15 Exam


Contact: Marie-Christine.Rousset"at" and pierre.gaillard"at"