Earth Data Science
This course covers the foundational aspects of data science, including data collection, cleaning, analysis, and visualization. Students will learn practical skills for working with real-world datasets oriented on earth data (seismic data, Lidar data, satellite images...)
Instructor: Antoine Lucas & Alexandre Fournier, Nobuaki Fuji, Grégory Sainton
Location: IPGP, 1 place Jussieu
Course Overview
This introductory course on machine learning covers fundamental concepts and algorithms in the field. By the end of this course, students will be able to:
- Understand key machine learning paradigms and concepts
- Implement basic machine learning algorithms using tensorflow and scikit-learn
- Evaluate and compare model performance
- Apply machine learning techniques to earth data
Prerequisites
- Basic knowledge of linear algebra and calculus
- Programming experience in Python
- Probability and statistics fundamentals
Textbooks
- Reference: “Machine learning avec Scikit-learn”, A. Geron
- Reference: “Pattern Recognition and Machine Learning” by Christopher Bishop
- Reference: “Deep leaning with Tensorflow” by A. Geron
Schedule
| Week | Date | Topic | Materials |
|---|---|---|---|
| 1 | Data for Earth science 1/2 Introduction to linear regression, other regression, and ACP. | ||
| 2 | Data for Earth science 2/2 Introduction to inference, inverse problem. | ||
| 3 | Crash course in deep learning (by Grégory Sainton) Introduction to neural networks, gradient descent, backprop, regularisation… | ||
| 4 | Code Lab 1 - Inspect data and play with regressions Learn how to clean improper or incomplete data. Apply several regression on them. | ||
| 5 | Code Lab 2 - Inverse problem on the Teil earthquake Apply inverse problem to find the epicenter of the earthquake | ||
| 6 | Code Lab 3 - LiDAR data analysis and ML segmentation Apply ML to analyse the multiscale data in LiDAR data | ||
| 7 | Assigment Lab in group in 4h |