Frontiers of Deep Learning for Engineers
CIVIL-611 / 4 crédits
Enseignant(s): Alahi Alexandre Massoud, Fink Olga, Tuia Devis
Langue: Anglais
Remark: Next time fall 2024, Max.16 persons, registration via https://forms.gle/uhhEXvbFeBfYAWf89
Summary
The seminar aims at discussing recent research papers in the field of deep learning, implementing the transferability/adaptability of the proposed approaches to applications in the field of research of the Ph.D. student.
Content
The class is structured into 2 parts. During the first part, students will present selected key ML papers. The papers are curated by the teaching staff based on their technical depth.
Then, in the second part of the course, the student will present their project using one of the presented methods for their own research of interest. Students have the freedom to pick their application of interest.
With the increasing amount of data collected in various domains, the importance of data science in many disciplines, such as infrastructure monitoring and management, transportation, spatial planning, structural and environmental engineering, has been increasing. The field is constantly developing further with numerous advances, extensions and modifications.
The course aims at discussing recent research papers in the field of machine learning, analyzing the transferability/adaptability of the proposed approaches to applications in the field of research and implementing the adapted algorithms to the field of research.
Each student will select a paper that is relevant for his/her research and present its content in the seminar, putting it into context, analyzing the assumptions, the transferability and generalizability of the proposed approaches. The students will also link the research content of the selected paper to their own research, evaluating the potential of transferring or adapting it. In the second part of the course, the students will implement, adapt and extend the selected algorithms. The students will work individually on their own project. Yet, the students will be reading each other's selected papers, providing feedback to each other.
Note
Register here:
https://forms.gle/uhhEXvbFeBfYAWf89
We will notify you of your registration some days before start.
Keywords
Deep learning, Machine learning
Learning Prerequisites
Required courses
A deep learning class (bachelor or master level)
Dans les plans d'études
- Nombre de places: 16
- Forme de l'examen: Exposé (session libre)
- Matière examinée: Frontiers of Deep Learning for Engineers
- Projet: 28 Heure(s)
- TP: 28 Heure(s)
- Type: optionnel
- Nombre de places: 16
- Forme de l'examen: Exposé (session libre)
- Matière examinée: Frontiers of Deep Learning for Engineers
- Projet: 28 Heure(s)
- TP: 28 Heure(s)
- Type: optionnel