Learn · Machine Learning for Earth System Sciences
Lecture 22 of 40Week 5
Lecture-22 : Hierarchical Bayesian Models for Spatio-Temporal Processes
Prof. Adway Mitra · IIT Kharagpur
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- Machine Learning for Earth System Sciences, IIT Kharagpur (at NPTEL)
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- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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All the lectures
- 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