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 - Paris - Salle Océane - Salle TP 1er étage
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 |