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NOC:Machine Learning for Earth System Sciences (Video)
Syllabus
Co-ordinated by :
IIT Kharagpur
Available from :
2022-05-16
Lec :
1
Modules / Lectures
Intro Video
week-01
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
week-02
Lecture 06 : Extreme Events
Lecture 07 : Extreme Value Theory
Lecture 08 : Causality
Lecture 09 : Networks
Lecture 10 : Data Assimilation
week-03
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
week-04
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
week-05
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
week-06
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
week-07
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
week-08
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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1
Lecture 01 : Introduction
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2
Lecture 02 : Basics of Spatio-Temporal Modeling
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3
Lecture 03 : Geostatistical Equation for Spatio-Temporal Process
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4
Lecture 04 : Gaussian Process Regression and Inverse Problems
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5
Lecture 05 : Anomaly Event Detection
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6
Lecture 06 : Extreme Events
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7
Lecture 07 : Extreme Value Theory
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8
Lecture 08 : Causality
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9
Lecture 09 : Networks
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10
Lecture 10 : Data Assimilation
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