| Course label : | Advanced Machine Learning 4 - Computer Vision |
|---|---|
| Teaching departement : | EEA / Electrotechnics - Electronics - Control Systems |
| Teaching manager : | Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS |
| Education language : | |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | MR_DS_S3_AM4 - Advanced machine learning 4 |
Education team
Teachers : Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS
External contributors (business, research, secondary education): various temporary teachers
Summary
• Image processing • Keypoints and Landmarks • Object classification and detection • Optical Flow – foundation and principles + deep learning approaches • Unsupervised Visual Feature Learning
Educational goals
After successfully taking this course, a student should be able to: · Understand the properties of visual data and the challenges associated to it · Master some fundamental tools in computer vision · Identify computer vision problems and leverage the right tools to solve them · Address current computer vision problems by employing state-of-the-art solutions
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: Labs, (min) 0 - 20 (max)
Exam, (min) 0 - 20 (max)
Passing grade 10/20
Online resources
R. Szeliski - Computer Vision: Algorithms and Applications, Springer 2010 R. Szeliski - Computer Vision: Algorithms and Applications – 2nd edition, Springer 2022
Pedagogy
24 hours, 12 lectures, 12 labs/tutorial sessions English is the default language.
Sequencing / learning methods
| Number of hours - Lectures : | 12 |
|---|---|
| Number of hours - Tutorial : | 12 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Machine Learning courses from M1 Data Science Signal Processing course from M1 Data Science