AI Luminaries
A curated collection of videos featuring top experts in artificial intelligence.
Overview
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.
Syllabus 8
- Week 1
- 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 2
- Lecture 06 : Extreme Events
- Lecture 07 : Extreme Value Theory
- Lecture 08 : Causality
- Lecture 09 : Networks
- Lecture 10 : Data Assimilation
- Week 3
- 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 4
- 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 5
- 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 6
- 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 7
- 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 8
- 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
Advantages and disadvantages
Advantages
- Taught by IIT and IISc professors, and it follows the Indian university syllabus closely.
- All videos and assignments are free on NPTEL and SWAYAM.
- The certificate is recognised by many Indian universities for credit transfer and by GATE aspirants.
- Great for GATE and semester exam preparation.
- Taught by Indian Institute of Technology Kharagpur, one of the strongest names in its field.
- Completely free.
- Self-paced: start any time.
- A clear syllabus (8 parts) you can see before you start.
Disadvantages
- The certificate needs a proctored exam at a centre, which has a fee.
- Recorded classroom lectures: thorough, but slower than made-for-online courses.
- New runs start on fixed dates (January and July).
- Learning is free, but the certificate costs money.
Some points apply to every course of this kind; see how we rank.
Free to learn
- Free: Every video and assignment is free on NPTEL and SWAYAM. Enrol when the next run opens.
- Certificate: Optional. It needs a proctored exam at a centre, which has a fee.
Before you start
Machine Learning (mandatory), Deep Learning (optional), a working idea of one or two domains in earth system sciences
Taught by
- Prof. Adway MitraIIT Kharagpur
Indian Institute of Technology Kharagpur is in Tier 1: world-leading universities and India's top institutes of our institution ranking (95/100).
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