Media Storage Type : 32 GB USB Stick
NPTEL Subject Matter Expert : Prof. Adway Mitra
NPTEL Co-ordinating Institute : IIT Kharagpur
NPTEL Lecture Count : 40
NPTEL Course Size : 4.3 GB
NPTEL PDF Text Transcription : Available and Included
NPTEL Subtitle Transcription : Available and Included (SRT)
Lecture Titles:
Lecture 1 - Introduction
Lecture 2 - Basics of Spatio-Temporal Modeling
Lecture 3 - Geostatistical Equation for Spatio-Temporal Process
Lecture 4 - Gaussian Process Regression and Inverse Problems
Lecture 5 - Anomaly Event Detection
Lecture 6 - Extreme Events
Lecture 7 - Extreme Value Theory
Lecture 8 - Causality
Lecture 9 - 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 - 1
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 and Course Conclusion