Syllabus

LEARNING OUTCOMES

The purpose of this special course is to demonstrate how machine learning methods can be used to tackle climate change.

Credits: 5

Schedule: 08.01.2024 - 15.02.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Olga Kuznetsova, Laia Amoros Carafi

Contact information for the course (applies in this implementation):

Laia Amorós (Finnish Meteorological Institute), Olga Kuznetsova (Aalto University), Shaikhum Monira (Aalto University, shaikhum.monira@aalto.fi)

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Workload

• applies in this implementation

  24 hours contact teaching (12 hours lectures and 12 hours of exercise sessions) and 111 hours of independent work. Overall, the idea is that after introducing the course and data in the first week, we focus on one policy area per week (e.g., energy efficiency measure in buildings and cities), discuss its importance and potential for climate action and then critically analyse the methodology and findings of a research article where ML was applied to that policy area. In the exercise session of the same week, we create an implementation of an ML method from the same family of algorithms (e.g., computer vision) to real-life data coming from the same policy area. 
  

DETAILS

Study Material

• applies in this implementation

  1. ML textbook
  2. ML for climate action 
  3. Research articles for each lecture:
  • Lecture II: D. P. Finch, P. I. Palmer, and T. Zhang (2022). Automated detection of atmospheric NO2 plumes from satellite data: a tool to help infer anthropogenic combustion emissions. Atmospheric Measurement Techniques, 15(3):721–733, 2022.
    
  • Lecture III: Schuit, B. J., Maasakkers, J. D., Bijl, P., Mahapatra, G., Van den Berg, A. W., Pandey, S., Aben, I. (2023). Automated detection and monitoring of methane super-emitters using satellite data. Atmospheric Chemistry and Physics Discussions, 1-47.
    
  • Lecture IV: Paolo, F., Kroodsma, D., Raynor, J. et al. Satellite mapping reveals extensive industrial activity at sea. Nature 625, 85–91 (2024). https://doi.org/10.1038/s41586-023-06825-8
  • Lecture V: 
  • Lecture VI: 

   

Substitutes for Courses

• valid for whole curriculum period:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  Prerequisites

FURTHER INFORMATION

Further Information

• valid for whole curriculum period:

  This is aimed at students who have some previous knowledge of Python and the main machine learning algorithms (at the level of the course CS-EJ3211 "Machine Learning with Python"). We will study relevant literature on different problems such as computer vision for remote sensing of farms, forests and emissions; causal inference for improving energy efficiency of buildings and cities, time-series forecasting for managing transport emissions, and unsupervised learning in electricity systems. Students will apply the methods to real data from satellites and other data sources.

  Target audience: primary - Master's in Machine Learning, Data Science and Artificial Intelligence; secondary: Master's in Business Analytics and Mathematics and Operations Research.

Details on the schedule

• applies in this implementation

  CS-E407519 Special Course in Machine Learning, Data Science and Artificial Intelligence: Machine Learning for Climate Action

  Class Schedule:
  

  1. Mon 08.01.2024  12:15 - 14:00, R001/Y313 
  2. Mon 15.01.2024  12:15 - 14:00, R001/Y313
  3. Mon 22.01.2024  12:15 - 14:00, R001/Y313
  4. Mon 29.01.2024  12:15 - 14:00, R001/Y313
  5. Mon 05.02.2024  12:15 - 14:00, R001/Y313
  6. Mon 12.02.2024  12:15 - 14:00, R001/Y313

  Exercise Session:

  1. Thu 11.01.2024  10:15 - 12:00, R037/1521-1522 AS6
  2. Thu 18.01.2024  10:15 - 12:00, R030/A136 T6
  3. Thu 25.01.2024  10:15 - 12:00, R037/1521-1522 AS6
  4. Thu 01.02.2024  10:15 - 12:00, R030/A136 T6
  5. Thu 08.02.2024  10:15 - 12:00, R030/A136 T6
  6. Thu 15.02.2024  10:15 - 12:00, R030/A136 T6

Description