Course Overview

This course provides an introduction to artificial intelligence
including its challenges, revolution, and achievements, and covers the
following:

- Machine Learning: principles, unsupervised vs supervised learning.
- Deep Learning: fundamentals, architectures, algorithms.

Learning Achievement

Competence

Course prerequisites

- First Bachelor year in Science.
- B2 level in English.

Grading Philosophy

Continuous assessment: oral presentations.

Course schedule

> Machine Learning:

- General principles
- Unsupervised learning: clustering
- Supervised learning: decision trees, random forests

> Deep Learning:

- Introduction and "sucess story"
- Fundamentals of deep learning
- Classic architectures and optimization algorithms

> Case studies:

- Illustration of methods with examples ofvisual information mining:
images and video

Course type

- 20 integrated lectures / tutorials (1h20) - 1 project during the semester (by pairs or trinominals)

Online Course Requirement

Instructor

Other information

Language of instruction: English
Mode of delivery: Face-to-face teaching

Site for Inquiry