Modules / Lectures


Sl.No Chapter Name MP4 Download
1Lecture 01 : IntroductionDownload
2Lecture 02 : Basics of Spatio-Temporal ModelingDownload
3Lecture 03 : Geostatistical Equation for Spatio-Temporal ProcessDownload
4Lecture 04 : Gaussian Process Regression and Inverse ProblemsDownload
5Lecture 05 : Anomaly Event DetectionDownload
6Lecture 06 : Extreme EventsDownload
7Lecture 07 : Extreme Value TheoryDownload
8Lecture 08 : CausalityDownload
9Lecture 09 : NetworksDownload
10Lecture 10 : Data AssimilationDownload
11Lecture 11 : Challenges and Opportunities for ML in ESSDownload
12Lecture 12 : Types of Machine Learning Problems in ESSDownload
13Lecture 13 : Convolutional Networks for Spatial ProblemsDownload
14Lecture 14 : Sequential Models for Temporal ProblemsDownload
15Lecture 15 : Probabilistic Models for Earth System ScienceDownload
16Lecture 16 : Identification of Indian Monsoon PredictorsDownload
17Lecture 17 : Statistical Downscaling of Rainfall with Machine LearningDownload
18Lecture 18 : Detection of Anomaly and Extreme EventsDownload
19Lecture 19 : Identifying Causal Relations from Time-Series - 1Download
20Lecture 20 : Identifying Causal Relations from Time-Series - 2Download
21Lecture 21 : Spatio-Temporal Modelling of ExtremesDownload
22Lecture 22 : Hierarchical Bayesian Models for Spatio-Temporal ProcessesDownload
23Lecture 23 : Geostatistical modelling for mapping based on in-situ measurementsDownload
24Lecture 24 : Nowcasting of Extreme Weather EventsDownload
25Lecture 25 : Discovering Clustered Weather PatternsDownload
26Lecture 26 : Interpretable Machine Learning for Earth System ScienceDownload
27Lecture 27 : Object Detection in Satellite ImageryDownload
28Lecture 28 : Object Detection in Satellite Imagery - 2Download
29Lecture 29 : Image Fusion from Multiple Sources for Remote SensingDownload
30Lecture 30 : Image Segmentation for Remote SensingDownload
31Lecture 31 : Satellite Imagery as a Proxy for Geophysical MeasurementsDownload
32Lecture 32 : Precipitation Nowcasting from Remote SensingDownload
33Lecture 33 : Deep Domain Adaptation for Remote SensingDownload
34Lecture 34 : Introduction to Earth System ModellingDownload
35Lecture 35 : Stochastic Weather GeneratorDownload
36Lecture 36 : Physics-Inspired Machine Learning for Process Models - 1Download
37Lecture 37 : Physics-Inspired Machine Learning for Process Models - 2Download
38Lecture 38 : Parameterizations for Sub-Grid Processes Using MLDownload
39Lecture 39 : Data Assimilation for Earth System Model CorrectionDownload
40Lecture 40 : ML for Climate Change Projection & Course ConclusionDownload

Sl.No Chapter Name English
1Lecture 01 : IntroductionDownload
Verified
2Lecture 02 : Basics of Spatio-Temporal ModelingDownload
Verified
3Lecture 03 : Geostatistical Equation for Spatio-Temporal ProcessDownload
Verified
4Lecture 04 : Gaussian Process Regression and Inverse ProblemsDownload
Verified
5Lecture 05 : Anomaly Event DetectionDownload
Verified
6Lecture 06 : Extreme EventsDownload
Verified
7Lecture 07 : Extreme Value TheoryDownload
Verified
8Lecture 08 : CausalityDownload
Verified
9Lecture 09 : NetworksDownload
Verified
10Lecture 10 : Data AssimilationDownload
Verified
11Lecture 11 : Challenges and Opportunities for ML in ESSDownload
Verified
12Lecture 12 : Types of Machine Learning Problems in ESSDownload
Verified
13Lecture 13 : Convolutional Networks for Spatial ProblemsDownload
Verified
14Lecture 14 : Sequential Models for Temporal ProblemsDownload
Verified
15Lecture 15 : Probabilistic Models for Earth System ScienceDownload
Verified
16Lecture 16 : Identification of Indian Monsoon PredictorsDownload
Verified
17Lecture 17 : Statistical Downscaling of Rainfall with Machine LearningDownload
Verified
18Lecture 18 : Detection of Anomaly and Extreme EventsDownload
Verified
19Lecture 19 : Identifying Causal Relations from Time-Series - 1Download
Verified
20Lecture 20 : Identifying Causal Relations from Time-Series - 2Download
Verified
21Lecture 21 : Spatio-Temporal Modelling of ExtremesPDF unavailable
22Lecture 22 : Hierarchical Bayesian Models for Spatio-Temporal ProcessesPDF unavailable
23Lecture 23 : Geostatistical modelling for mapping based on in-situ measurementsPDF unavailable
24Lecture 24 : Nowcasting of Extreme Weather EventsPDF unavailable
25Lecture 25 : Discovering Clustered Weather PatternsPDF unavailable
26Lecture 26 : Interpretable Machine Learning for Earth System SciencePDF unavailable
27Lecture 27 : Object Detection in Satellite ImageryPDF unavailable
28Lecture 28 : Object Detection in Satellite Imagery - 2PDF unavailable
29Lecture 29 : Image Fusion from Multiple Sources for Remote SensingPDF unavailable
30Lecture 30 : Image Segmentation for Remote SensingPDF unavailable
31Lecture 31 : Satellite Imagery as a Proxy for Geophysical MeasurementsPDF unavailable
32Lecture 32 : Precipitation Nowcasting from Remote SensingPDF unavailable
33Lecture 33 : Deep Domain Adaptation for Remote SensingPDF unavailable
34Lecture 34 : Introduction to Earth System ModellingPDF unavailable
35Lecture 35 : Stochastic Weather GeneratorPDF unavailable
36Lecture 36 : Physics-Inspired Machine Learning for Process Models - 1PDF unavailable
37Lecture 37 : Physics-Inspired Machine Learning for Process Models - 2PDF unavailable
38Lecture 38 : Parameterizations for Sub-Grid Processes Using MLPDF unavailable
39Lecture 39 : Data Assimilation for Earth System Model CorrectionPDF unavailable
40Lecture 40 : ML for Climate Change Projection & Course ConclusionPDF unavailable


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