Machine Learning Intro Course Materials#
Welcome to “Machine Learning: Foundations and Applications” a comprehensive reader designed to guide you through the fundamental concepts and practical applications of machine learning in the context of land use change. This reader is an integral part of our courses at TU Berlin provided by the department of “Artificial Intelligence and Land Use Change,” where we explore the power of artificial intelligence in understanding climate change dynamics in application to land use.
Check out our TU page for more information about our courses.
Motivation#
Artificial intelligence (AI) and machine learning (ML) have become indispensable tools in modern scientific research and practical applications. From predicting land cover changes, agricultural practices or meteorolgical applications these technologies offer opportunities to make informed decisions that can lead to sustainable land use management. This reader aims to equip you with the essential knowledge and skills required to harness the potential of AI and ML in this field. This reader is structured to provide a balanced blend of theory and practice. It begins with an introduction to Python, which will be your primary tool throughout this journey. Python’s simplicity and extensive libraries make it an ideal choice for implementing machine learning algorithms and handling large datasets. We will then get into q brief Recap of Statistical knowledge, indispensable for building high quality machine learning models. Hands-on examples, will be provided throughout the entire reader, to deepen your understanding.
Getting Started#
To get the most out of this reader, it is recommended that you have a basic understanding of programming and mathematics. However, the material is designed to be accessible to beginners, with step-by-step explanations and hands-on exercises. To set up your Python environment with all the package requirements needed for this reader, a requirements file is provided here.
Welcome to the course, and happy learning!
Acknowledgements#
This reader is heavily based on the SOGA E-Learning projected by the Freie Universität Berlin. It is really nice comprehensive guide on Statistics in Application to Earth Sciences and geodata analysis. We really recommend to check it out!
Rudolph, A., Krois, J., Hartmann, K. (2023): Statistics and Geodata Analysis using Python (SOGA-Py). Department of Earth Sciences, Freie Universitaet Berlin.