What you will learn
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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
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19:11
Lecture 02 : Basics of Spatio-Temporal Modeling
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29:47
Lecture 03 : Geostatistical Equation for Spatio-Temporal Process
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27:06
Lecture 04 : Gaussian Process Regression and Inverse Problems
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30:44
Lecture 05 : Anomaly Event Detection
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30:57
Lecture 11 : Challenges and Opportunities for ML in ESS
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29:56
Lecture 12 : Types of Machine Learning Problems in ESS
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33:00
Lecture 13 : Convolutional Networks for Spatial Problems
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34:07
Lecture 14 : Sequential Models for Temporal Problems
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29:13
Lecture 15 : Probabilistic Models for Earth System Science
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32:22
Lecture 16 : Identification of Indian Monsoon Predictors
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30:54
Lecture 17 : Statistical Downscaling of Rainfall with Machine Learning
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33:32
Lecture 18 : Detection of Anomaly and Extreme Events
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32:58
Lecture 19 : Identifying Causal Relations from Time-Series - 1
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30:49
Lecture 20 : Identifying Causal Relations from Time-Series - 2
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33:41
Lecture 21 : Spatio-Temporal Modelling of Extremes
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37:00
Lecture 22 : Hierarchical Bayesian Models for Spatio-Temporal Processes
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28:16
Lecture 23 : Geostatistical modelling for mapping based on in-situ measurements
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35:53
Lecture 24 : Nowcasting of Extreme Weather Events
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32:47
Lecture 25 : Discovering Clustered Weather Patterns
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36:35
Lecture 26 : Interpretable Machine Learning for Earth System Science
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37:42
Lecture 27 : Object Detection in Satellite Imagery
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37:42
Lecture 28 : Object Detection in Satellite Imagery - 2
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34:21
Lecture 29 : Image Fusion from Multiple Sources for Remote Sensing
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35:53
Lecture 30 : Image Segmentation for Remote Sensing
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33:24
Lecture 31 : Satellite Imagery as a Proxy for Geophysical Measurements
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33:58
Lecture 32 : Precipitation Nowcasting from Remote Sensing
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36:17
Lecture 33 : Deep Domain Adaptation for Remote Sensing
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36:37
Lecture 34 : Introduction to Earth System Modelling
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36:00
Lecture 35 : Stochastic Weather Generator
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37:55
Lecture 36 : Physics-Inspired Machine Learning for Process Models - 1
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37:55
Lecture 37 : Physics-Inspired Machine Learning for Process Models - 2
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36:06
Lecture 38 : Parameterizations for Sub-Grid Processes Using ML
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39:31
Lecture 39 : Data Assimilation for Earth System Model Correction
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36:58
Lecture 40 : ML for Climate Change Projection & Course Conclusion
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39:56
Rajath Arikeri
when will i get the exam
4 months ago
0
Rajath Arikeri
when will i get the exam
4 months ago
0
Devarala Siva sai gandhi
even i didnot get
4 months ago
0
5.0
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Pooja N M
5 months ago
When I will receive certificate
Meet Your Instructor
Free
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Course Duration22 h 17 m 8 s
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Course LevelIntermediate
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Student Enrolled115
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LanguageEnglish
This Course Includes
- 22 h 17 m 8 s Video Lectures
- 0 Quizzes
- 0 Assignments
- 0 Downloadable Resources
- Full Lifetime Access
- Certificate of Completion
Rajath Arikeri
when will i get the exam