BIP Applied Machine Learning: from Fundamentals to Sustainable Applications

This Blended Intensive Program (BIP) course provides a comprehensive introduction to applied machine learning (ML), bridging foundational concepts with cutting-edge, real-world applications in sustainability

The course is designed for master's and PhD students from all backgrounds, the course equips learners with both theoretical understanding and hands-on skills essential for solving complex problems using ML.

The program features expert-led sessions that explore cutting-edge machine learning applications in predictive maintenance, healthcare, and agriculture, offering practical insights and real-world case studies from these critical domains.

In this BIP course you will learn online and face to face with other students from our nine EU GREEN partner universities. The in-presence attendance takes place at the University of Evora, Portugal.

Content

  1. Overview of Machine Learning with focus on sustainable applications
  2. Smart Prediction with Supervised Learning
  3. Unsupervised Learning such as Clustering Methods
  4. Introduction to Deep Learning and Convolutional Neural Networks (CNNs)
  5. Explainable Artificial Intelligence (XAI) and Physics-Informed Artificial Intelligence (PIAI)
  6. Machine learning applications

General Information about the Course

The course is open for PhD and master students who wish to learn Machine learning.

Learning Outcomes

  1. Explain basic concepts in machine learning and deep learning
  2. Apply common algorithms in supervised and unsupervised learning
  3. Use tools for explainable AI and understand the basics of physics-informed AI
  4. Implement and train neural networks, including convolutional networks (CNNs)
  5. Analyze applications of machine learning in real-world scenarios with a focus on sustainability, energy, industry, agriculture, and health.

Tuition fee: free of charge for students registrerad in EU GREEN University or University in other EES country.

Duration: October 1-November 14.
Virtual sessions: October 1-24 and November 3-14
IN-presence attendance at the University of Evora: October 27-October 31

Language of instruction: English

ECTS for participation in the programme: 6 ECTS credits

Number of places: 25

Deadline for application: July 18

Conditions for Admission

The course is open for PhD and master students who wish to learn Machine learning.

Level of English: B2 or equivalent
Common European Framework of Reference for Language skills | Europass Länk till annan webbplats.

Students should complete the online application form by July 18.

The project has been registered in the beneficiary module.
The code is: 2024-1-SE01-KA131-HED-000198865-1

APPLICATION FORM

Students should send the following documents to: Sunilkumar Telagam Setti by July 18.

  • Transcript of records and certificate of enrolment
  • Personal motivation letter, up to 200 words

For more information about the course and the application, please contact Sunilkumar Telegam Setti.

Financial Support

The participation in Blended Intensive Programme may be covered by an ERASMUS+ SMS Short Mobility Grant for all participating students. This financial support may only be guaranteed by the selected student’s home University. Please refer to your local coordinator or Erasmus+/International Relations Office for any further information related to the financial support made available.

Students enrolled in a University in a non-EES country will be subject to a course fee.

Contacts

Sunil Kumar, University of Gävle
sunilkumar.telagam.setti@hig.se

 

Giulia Conti, University of Parma
giulia.conti@unipr.it

 

Mouhaydine Tlemcani, University of Evora
tlem@uevora.pt

Sidan uppdaterades 2025-06-23