aradfarahani/KhUAdvanceGeoML

The Advance Python and Machine Learning workshop was organized by the Geology Society of Kharazmi University with collaboration of Water Research Center from April to June at Kharazmi University by Nima Raeisi and Mahdi Farmahini Farahani.

KhUAdvanceGeoML

Advanced Python and Machine Learning Workshop(for earth science students)

Overview

Welcome to the Advanced Python and Machine Learning Workshop README. This workshop is organized by the Geology Society of Kharazmi University in collaboration with the Water Research Center. It is designed to provide participants with in-depth knowledge and practical skills in Python programming and machine learning applications.

Instructors

• Nima Raeisi Graduate Hydrogeology Student @ KhU
  
  https://orcid.org/0009-0003-7073-0068
• Mahdi Farmahini Farahani Undergraduate Geology Student @ KhU
  
  https://orcid.org/0009-0008-3800-8688

Duration

April to June 2024

Location

Kharazmi University Faculty of Earth Science

Objectives

The workshop aims to:

• Enhance participants' Python coding skills.
• Provide a comprehensive understanding of machine learning algorithms.
• Apply machine learning techniques to real-world geoscience problems.
• Foster collaboration among students and professionals in the field of geology.

Prerequisites

Participants are expected to have:

• Basic knowledge of Python programming.
• Understanding of fundamental machine learning concepts.
• A laptop with Python and relevant libraries installed.

Program

The workshop includes the following modules:

1. Python Programming Refresher
2. Data Analysis and Visualization with Python
3. Introduction to Machine Learning
4. Supervised and Unsupervised Learning
5. Model Evaluation and Selection
6. Machine Learning in Geosciences

Materials

Workshop materials, including slides, datasets, and code examples, will be provided.

Reference

• Petrelli, M. (2021). Introduction to python in earth science data analysis: from descriptive statistics to machine learning. Springer Nature.

• Petrelli, M. (2023). Machine Learning for Earth Sciences. Springer Nature.

• Trauth, M. H. (2022). Python recipes for earth sciences. Springer Nature.

• Muñoz Sabater, J. (2019): ERA5-Land monthly averaged data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.68d2bb30 (Accessed on 03-06-2024)

Registration

For registration details, please contact the Geology Society at Kharazmi University.

Acknowledgments

We would like to thank the Geological Society of Kharazmi University and the Water Research Center for their support in making this workshop possible.

Contact

For more information, please reach out to the workshop coordinators.

We look forward to seeing you at the workshop and exploring the exciting world of Python and Machine Learning together!