Machine Learning Applications in Geosciences (GP 431)

Course Name	Machine Learning Applications in Geosciences
Course ID	GP 431
Description	Department Elective 5 (Theory)
Syllabus	Overview of machine learning concepts and techniques. Importance and applications of machine learning in geosciences. Data cleaning and preprocessing techniques for geoscience datasets. Feature selection and extraction methods. Handling missing data and outliers. Introduction to geospatial data processing and analysis. Supervised Learning Techniques. Unsupervised Learning Techniques. Introduction to Generative Adversarial Networks (GAN). Deep Learning in Geosciences. Geospatial Data Analysis with Machine Learning. Application of Machine learning techniques in timeseries forecasting. Case Studies and Applications.
References	Sebastian Raschka and Vahid Mirjalili, Python Machine Learning, Packt Publishing, 2017 Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly Media, 2019 Michael Bowles, Machine Learning in Python: Essential Techniques for Predictive Analysis, Wiley, 2015 Robin Lovelace, Jakub Nowosad, and Jannes Muenchow Geocomputation with R, CRC Press, 2019 Benjamin Moseley, Lion Krischer, Machine Learning and Artificial Intelligence in Geosciences, 1st Edition, Volume 61 - September 22, 2020