Duration
10h Th, 20h Pr
Number of credits
| Master MSc. in Data Science, professional focus | 5 crédits | |||
| Master MSc. in Data Science and Engineering, professional focus | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course introduces advanced machine learning techniques applied to gravitational-wave astronomy. Students will learn to identify and analyze gravitational-wave signals in noisy environments using machine learning algorithms.
The course will cover the following topics, which are tentative and subject to change based on emerging research:
- Gravitational-Wave Detection: Methods to detect gravitational waves from various astrophysical sources, including binary black holes and neutron stars.
- Signal Processing and Denoising: Techniques such as Sparse Dictionary Learning and Autoencoders to denoise gravitational wave signals and remove glitches from interferometer data.
- Time-Series Analysis: Long Short-Term Memory Networks (LSTMs) for processing time-series data, focusing on detecting transient gravitational-wave signals.
- Convolutional Neural Networks (CNNs): Applications of CNNs to recognize patterns in gravitational wave data, including time-frequency maps for signal classification and identification.
- Anomaly Detection: Using Convolutional Autoencoders for detecting anomalies in data, such as noise and glitches that impact the quality of gravitational-wave detections.
- Generative Adversarial Networks (GANs): Exploring the use of GANs to generate synthetic data for training and testing machine learning models, particularly for noise generation and data augmentation.
- XGBoost: Applying this powerful gradient boosting technique for classification and regression tasks in gravitational wave data analysis.
Learning outcomes of the learning unit
By the end of the course, students will be able to:
- Understand and apply advanced machine learning techniques to real gravitational-wave data.
- Analyze and classify noisy datasets from gravitational wave interferometers.
- Develop machine learning models for detecting, denoising, and classifying gravitational wave signals.
- Implement neural networks and boosting algorithms to solve complex challenges in gravitational wave astronomy.
Prerequisite knowledge and skills
Students should have a solid foundation in Python programming and basic machine learning concepts. Prior knowledge of gravitational-wave astronomy is also recommended.
Planned learning activities and teaching methods
The course will combine lectures with practical hands-on projects. Students will work on real gravitational-wave datasets using Jupyter notebooks. Group discussions and student presentations will also be included to encourage collaborative learning.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Further information:
The course will be delivered in person, with flexibility for online discussions or assignments as needed.
Course materials and recommended or required readings
Platform(s) used for course materials:
- MyULiège
Further information:
Course materials will include selected readings from recent research papers on machine learning applied to gravitational-wave data, as well as Python tutorials for implementing the machine learning models.
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Further information:
- An oral exam to assess students' understanding of both gravitational-wave concepts and the machine learning techniques discussed during the course.
- A homework assignment that will be graded, focused on applying machine learning methods to gravitational-wave data.
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Maxime Fays
(maxime.fays@uliege.be)
Room 4.43 Bât. B5A
Inter. fondamentales en physique et astrophysique (IFPA)
Quartier Agora allée du six Août 19
4000 Liège
Téléphone de service: +32 4 3663643