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Course10610523840 lectures

Machine Learning for Earth System Sciences

Prof. Adway Mitra · IIT Kharagpur

Start with lecture 1 At NPTEL

About

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.

Intended audience: Final year undergraduate, Postgraduate and research students

Prerequisites: Machine Learning (mandatory), Deep Learning (optional), a working idea of one or two domains in earth system sciences

Lectures

Week 1

  1. Lecture 01 : Introduction YouTube
  2. Lecture 02 : Basics of Spatio-Temporal Modeling YouTube
  3. Lecture 03 : Geostatistical Equation for Spatio-Temporal Process YouTube
  4. Lecture 04 : Gaussian Process Regression and Inverse Problems YouTube
  5. Lecture 05 : Anomaly Event Detection YouTube

Week 2

  1. Lecture 06 : Extreme Events YouTube
  2. Lecture 07 : Extreme Value Theory YouTube
  3. Lecture 08 : Causality YouTube
  4. Lecture 09 : Networks YouTube
  5. Lecture 10 : Data Assimilation YouTube

Week 3

  1. Lecture-11 : Challenges and Opportunities for ML in ESS YouTube
  2. Lecture-12 : Types of Machine Learning Problems in ESS YouTube
  3. Lecture-13 : Convolutional Networks for Spatial Problems YouTube
  4. Lecture-14 : Sequential Models for Temporal Problems YouTube
  5. Lecture-15 : Probabilistic Models for Earth System Science YouTube

Week 4

  1. Lecture-16 : Identification of Indian Monsoon Predictors YouTube
  2. Lecture-17 : Statistical Downscaling of Rainfall with Machine Learning YouTube
  3. Lecture-18 : Detection of Anomaly and Extreme Events YouTube
  4. Lecture-19 : Identifying Causal Relations from Time-Series - 1 YouTube
  5. Lecture-20 : Identifying Causal Relations from Time-Series - 2 YouTube

Week 5

  1. Lecture-21 : Spatio-Temporal Modelling of Extremes YouTube
  2. Lecture-22 : Hierarchical Bayesian Models for Spatio-Temporal Processes YouTube
  3. Lecture-23 : Geostatistical modelling for mapping based on in-situ measurements YouTube
  4. Lecture-24 : Nowcasting of Extreme Weather Events YouTube
  5. Lecture-25 : Discovering Clustered Weather Patterns YouTube

Week 6

  1. Lecture-26 : Interpretable Machine Learning for Earth System Science YouTube
  2. Lecture-27 : Object Detection in Satellite Imagery YouTube
  3. Lecture-28 : Object Detection in Satellite Imagery - 2 YouTube
  4. Lecture-29 : Image Fusion from Multiple Sources for Remote Sensing YouTube
  5. Lecture-30 : Image Segmentation for Remote Sensing YouTube

Week 7

  1. Lecture-31 : Satellite Imagery as a Proxy for Geophysical Measurements YouTube
  2. Lecture-32 : Precipitation Nowcasting from Remote Sensing YouTube
  3. Lecture-33 : Deep Domain Adaptation for Remote Sensing YouTube
  4. Lecture-34 : Introduction to Earth System Modelling YouTube
  5. Lecture-35 : Stochastic Weather Generator YouTube

Week 8

  1. Lecture-36 : Physics-Inspired Machine Learning for Process Models - 1 YouTube
  2. Lecture-37 : Physics-Inspired Machine Learning for Process Models - 2 YouTube
  3. Lecture-38 : Parameterizations for Sub-Grid Processes Using ML YouTube
  4. Lecture-39 : Data Assimilation for Earth System Model Correction YouTube
  5. Lecture-40 : ML for Climate Change Projection & Course Conclusion YouTube

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Kharagpur
Department
Computer Science and Engineering
Teacher
Prof. Adway Mitra
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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