VTU Online Class - Course Details

Machine Learning For Earth System Sciences

ADWAY MITRA

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(0) 69 Students

What you will learn

Computer Science and Engineering

This course will start with a gentle recapitulation of relevant concepts of spatio-temporal statistics and data mining, following which it will take up the topics of earth system observations, earth system data analytics and earth system modeling in various domains, such as hydrology, climate and soil.

Lecture 01 : Introduction

Lecture 02 : Basics of Spatio-Temporal Modeling

Lecture 03 : Geostatistical Equation for Spatio-Temporal Process

Lecture 04 : Gaussian Process Regression and Inverse Problems

Lecture 05 : Anomaly Event Detection

Lecture 06 : Extreme Events

Lecture 07 : Extreme Value Theory

Lecture 08 : Causality

Lecture 09 : Networks

Lecture 10 : Data Assimilation

Lecture 11 : Challenges and Opportunities for ML in ESS

Lecture 12 : Types of Machine Learning Problems in ESS

Lecture 13 : Convolutional Networks for Spatial Problems

Lecture 14 : Sequential Models for Temporal Problems

Lecture 15 : Probabilistic Models for Earth System Science

Lecture 16 : Identification of Indian Monsoon Predictors

Lecture 17 : Statistical Downscaling of Rainfall with Machine Learning

Lecture 18 : Detection of Anomaly and Extreme Events

Lecture 19 : Identifying Causal Relations from Time-Series - 1

Lecture 20 : Identifying Causal Relations from Time-Series - 2

Lecture 21 : Spatio-Temporal Modelling of Extremes

Lecture 22 : Hierarchical Bayesian Models for Spatio-Temporal Processes

Lecture 23 : Geostatistical modelling for mapping based on in-situ measurements

Lecture 24 : Nowcasting of Extreme Weather Events

Lecture 25 : Discovering Clustered Weather Patterns

Lecture 26 : Interpretable Machine Learning for Earth System Science

Lecture 27 : Object Detection in Satellite Imagery

Lecture 28 : Object Detection in Satellite Imagery - 2

Lecture 29 : Image Fusion from Multiple Sources for Remote Sensing

Lecture 30 : Image Segmentation for Remote Sensing

Lecture 31 : Satellite Imagery as a Proxy for Geophysical Measurements

Lecture 32 : Precipitation Nowcasting from Remote Sensing

Lecture 33 : Deep Domain Adaptation for Remote Sensing

Lecture 34 : Introduction to Earth System Modelling

Lecture 35 : Stochastic Weather Generator

Lecture 36 : Physics-Inspired Machine Learning for Process Models - 1

Lecture 37 : Physics-Inspired Machine Learning for Process Models - 2

Lecture 38 : Parameterizations for Sub-Grid Processes Using ML

Lecture 39 : Data Assimilation for Earth System Model Correction

Lecture 40 : ML for Climate Change Projection & Course Conclusion

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Meet Your Instructor

video

Free

Course Duration

22 h 17 m 8 s
Course Level

Intermediate
Student Enrolled

69
Language

English

This Course Includes

22 h 17 m 8 s Video Lectures
0 Quizzes
0 Assignments
0 Downloadable Resources
Full Lifetime Access
Certificate of Completion