-
Name (Acronym)Artificial Intelligence and Machine Learning Methods for Environmental Applications (AIENV)
-
ECTS6
-
Contact E-mailmarco.salucci@unitn.it
-
Teacher
-
Syllabus
-
Institution
-
Study Program
-
Degree
-
LanguageEnglish
-
Tracks
ABSTRACT
The course provides fundamental knowledge about Artificial Intelligence (AI) and Machine Learning (ML) and their applications in environmental engineering. While being based on rigorous theoretical content, the course is oriented towards the most relevant applications for environmental engineers. To complete the didactic offer, various numerical exercises (exploiting SW programs) will follow the theoretical lessons.COURSE CONTENT
Part 1: THE "THREE-STEPS LEARNING-BY-EXAMPLES (LBE)" FRAMEWORK
- Overview, general concepts, and taxonomy of Machine Learning (ML) methodologies
- Interpolation techniques
- Dimensionality reduction methodologies
- Space exploration/sampling methodologies
- Classification and regression methodologies
Part 2: APPLICATIONS OF AI AND ML IN ENVIRONMENTAL ENGINEERING
- Overview of AI and ML methods for environmental applications:
- Water quality detection
- Hydrology and prediction/optimization of water resource availability
- Prediction of lake surface temperature
- Environmental remote sensing
- Soil science and agriculture
TEACHING ACTIVITIES
- Theoretical Lessons
- e-Xam Self Assessment (each teaching class or periodically)
- MATLAB Hands-On
- e-Xam Final Assessment
FURTHER READINGS
- M. Z. Naser, Machine Learning for Civil & Environmental Engineers. Wiley, 2023.
- S. Araghinejad, Data-driven modeling: using MATLAB in water resources and environmental engineering. Springer, 2014.
- A. I. J. Forrester, A. Sobester, and A. J. Keane, Engineering Design via Surrogate Modelling: A Practical Guide. Hoboken, N.J.: John Wiley & Sons, 2008.
- T. Hastie, J. Friedman, and R. Tisbshirani, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer, 2017.
- V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 2000.
Course Information
Date: | 25 November - 6 December, 2024 (2 weeks, 30 hours/week) |
Format: | The course is offered in blended form (onsite and online) |
Registration Information
UniTN Students: | Free |
EXTERNAL Students: |
216 Eu: First course 180 Eu: Every course from the second one |
The fees include the course teaching, the slides/material, and the video recordings.
Registration Procedure for UniTN Students
Please contact the Student Support Office of your Department/Centre/School to include the course in your study plan.
Registration Procedure for EXTERNAL Students
Step 1: Register a "guest" type account (@guest.unitn.it)
- Should you still not have a UniTN account, you have to register and log in with your SPID identity or CIE (electronic ID card). If you cannot use SPID or CIE, please create your own UniTN account.
Step 2: Enroll to a Single UniTN Course
-
Complete the online application through the webpage apply for enrollment in ‘Standard’ single classes a.y. 2024/2025.
In the application form (Section "Teaching Activities") put the following information:- Name of single class/teaching activity: Artificial Intelligence and Machine Learning Methods for Environmental Applications
- Code of single class/teaching activity: 140711
- Degree course to which the teaching activity is associated: [0332H] Ingegneria per l’Ambiente e il Territorio
- Once received the outcome of the application (1-3 days), login into ESSE3 with your "guest" account user-name and password. Then, pay the bulletin you find in Administrative Office – Payments.
NOTES:
- A vademecum with a step-by-step guide to enroll to a single course at the University of Trento is available here
- For any question on the registration process, please write to contact@eledia.org