East-African School for Young Researchers on Advanced Machine Learning Techniques

Africa/Nairobi
University of Nairobi, Chiromo Campus

University of Nairobi, Chiromo Campus

Dimitris Varouchas (LAL Orsay), Ian Kaniu (University of Nairobi), Lydia Roos (CNRS/IN2P3 - LPNHE)
Description

The East-African School for Young Researchers on Advanced Machine Learning Techniques (EASY-ML) was held at University of Nairobi, Chiromo Campus, from July 9th to 22nd, 2025. 

The school is designed to introduce participants to core concepts, algorithms, and techniques in machine learning, with a focus on their application in scientific research. Through a combination of lectures and hands-on sessions, participants will gain practical experience in using machine learning tools to analyze data and address complex problems within their respective fields. This training aims to foster both innovative research approaches and effective problem-solving skills. Forty participants are selected based on the quality of their research and how machine learning can enhance it. It also prioritize diversity, with representation from Kenyan and other East African universities and research institutions, multidisciplinary backgrounds, and gender balance. In addition to the core program, the school includes networking sessions, an excursion, and a poster session for participants to present their work.

Facilitators come from France, South Africa and Kenya.

Main Topics

  • Introduction to Machine Learning
  • Ethics in Artificial Intelligence and Machine Learning
  • Regression and Classification
  • Artificial Neural Networks
  • Unsupervised Learning
  • Advanced Machine Learning Techniques (Convolutional Neural Networks, Boosted Decision Trees, etc.)
  • Hands-on Practice with PyTorch
  • Applied Machine-Learning Case Studies
  • Participant-Led Projects

 

Short URL: https://indico.in2p3.fr/e/easy-ml

 

    • 8:00 AM 8:30 AM
      Registration 30m
    • 8:30 AM 9:00 AM
      Welcome address 30m
      Speaker: Ian Kaniu (University of Nairobi)
    • 9:00 AM 10:30 AM
      What is Artificial Intelligence? Machine Learning? + Ethics 1h 30m

      Morning session I: What is Artificial Intelligence? Machine learning? + Ethics

      Speaker: Claire David (AIMS South Africa)
    • 10:30 AM 11:00 AM
      Coffee and tea break 30m
    • 11:00 AM 12:30 PM
      Linear Regression 1h 30m

      Morning Session II: Application, discussion, hands-on practice

      Speaker: Claire David (AIMS South Africa)
    • 12:30 PM 2:00 PM
      Lunch 1h 30m
    • 2:00 PM 3:30 PM
      Linear Regression (Continued) + Tutorial 1h 30m

      Afternoon Session I: More hands-on: coding lab, group work, demos

      Speakers: Claire David (AIMS South Africa), Edna Milgo (Open University of Kenya/ UoN)
    • 3:30 PM 4:00 PM
      Coffee and tea break 30m
    • 4:00 PM 5:30 PM
      Python Primer + Tutorial 1h 30m

      Afternoon Session II: Light content: Q&A, recap, open discussion

      Speaker: Edna Milgo (Open University of Kenya/ UoN)
    • 5:30 PM 6:00 PM
      Introduction to the project session 30m
    • 7:30 AM 8:00 AM
      Daily registration 30m
    • 8:00 AM 9:30 AM
      Classification 1/2 1h 30m

      Morning Session I: High-focus content (theory-heavy or technical demos)

      Speaker: Edna Milgo (Open University of Kenya/ UoN)
    • 9:30 AM 10:30 AM
      Official opening 1h
    • 10:30 AM 11:00 AM
      Coffee and tea break 30m
    • 11:00 AM 12:30 PM
      Classification 2/2 + Performance Metrics 1h 30m

      Morning Session II: Application, discussion, hands-on practice

      Speaker: Edna Milgo (Open University of Kenya/ UoN)
    • 12:30 PM 2:00 PM
      Lunch 1h 30m
    • 2:00 PM 3:30 PM
      Assignment: Make Your Own Classifier! 1h 30m

      Afternoon Session I: More hands-on: coding lab, group work, demos

      Speaker: Claire David (AIMS South Africa)
    • 3:30 PM 4:00 PM
      Coffee and tea break 30m
    • 4:00 PM 5:30 PM
      Assignment (Continued) + Bonus (SVM) 1h 30m

      Afternoon Session II: Light content: Q&A, recap, open discussion

      Speakers: Claire David (AIMS South Africa), Edna Milgo (Open University of Kenya/ UoN)
    • 5:30 PM 7:00 PM
      Free Time / Networking / Group work assignments 1h 30m
    • 8:00 AM 8:30 AM
      Daily registration 30m
    • 8:30 AM 10:30 AM
      Review + Introduction to Neural Networks 2h

      Morning Session I: High-focus content (theory-heavy or technical demos)

      Speaker: Claire David (AIMS South Africa)
    • 10:30 AM 11:00 AM
      Coffee and tea break 30m
    • 11:00 AM 12:30 PM
      Perceptron 1h 30m

      Morning Session II: Application, discussion, hands-on practice

      Speaker: Edna Milgo (Open University of Kenya/ UoN)
    • 12:30 PM 2:00 PM
      Lunch 1h 30m
    • 2:00 PM 3:30 PM
      Forward Propagation 1h 30m

      Afternoon Session I: More hands-on: coding lab, group work, demos

      Speaker: Claire David (AIMS South Africa)
    • 3:30 PM 4:00 PM
      Coffee and tea break 30m
    • 4:00 PM 5:30 PM
      Forward Propagation, by Hand! 1h 30m

      Afternoon Session II: Light content: Q&A, recap, open discussion

      Speaker: Claire David (AIMS South Africa)
    • 5:30 PM 7:00 PM
      Poster session 1h 30m
    • 8:00 AM 8:30 AM
      Daily registration 30m
    • 8:30 AM 10:30 AM
      Backpropagation 2h

      Morning Session I: High-focus content (theory-heavy or technical demos)

      Speaker: Claire David (AIMS South Africa)
    • 10:30 AM 11:00 AM
      Coffee and tea break 30m
    • 11:00 AM 12:30 PM
      Towards Deep Learning 1h 30m

      Morning Session II: Application, discussion, hands-on practice

      Speaker: Claire David (AIMS South Africa)
    • 12:30 PM 2:00 PM
      Lunch 1h 30m
    • 2:00 PM 3:30 PM
      Kick-start Assignment 1h 30m

      Afternoon Session I: More hands-on: coding lab, group work, demos

      Speakers: Claire David (AIMS South Africa), Edna Milgo (Open University of Kenya/ UoN)
    • 3:30 PM 4:00 PM
      Coffee and tea break 30m
    • 4:00 PM 5:30 PM
      Code a Neural Network by Hand! 1h 30m

      Afternoon Session II: Light content: Q&A, recap, open discussion

      Speakers: Claire David (AIMS South Africa), Edna Milgo (Open University of Kenya/ UoN)
    • 5:30 PM 7:00 PM
      Free Time / Networking / Group work assignments 1h 30m
    • 8:00 AM 7:00 PM
      Excursion 11h
    • 8:00 AM 8:30 AM
      Daily registration 30m
    • 8:30 AM 10:30 AM
      Ethics in AI 2h

      Morning Session I: High-focus content (theory-heavy or technical demos)

      Speaker: Claire David (AIMS South Africa)
    • 10:30 AM 11:00 AM
      Coffee and tea break 30m
    • 11:00 AM 12:30 PM
      Debate 1h 30m

      Morning Session II: Application, discussion, hands-on practice

      Speakers: Claire David (AIMS South Africa), Edna Milgo (Open University of Kenya/ UoN)
    • 12:30 PM 2:00 PM
      Lunch 1h 30m
    • 2:00 PM 3:30 PM
      Unsupervised Learning 1h 30m

      Artificial Neural Networks Afternoon Session I: More hands-on: coding lab, group work, demos

      Speakers: Charles Ndegwa, Patrick Gikunda
    • 3:30 PM 4:00 PM
      Coffee and tea break 30m
    • 4:00 PM 5:30 PM
      Unsupervised Learning 1h 30m

      Afternoon Session II: Light content: Q&A, recap, open discussion

      Speakers: Charles Ndegwa, Patrick Gikunda
    • 5:30 PM 7:00 PM
      Free Time / Networking / Group work assignments 1h 30m
    • 8:00 AM 8:30 AM
      Daily registration 30m
    • 8:30 AM 10:30 AM
      Unsupervised Learning 2h

      Morning Session I: High-focus content (theory-heavy or technical demos)

      Speakers: Charles Ndegwa, Patrick Gikunda
    • 10:30 AM 11:00 AM
      Coffee and tea break 30m
    • 11:00 AM 12:30 PM
      Unsupervised Learning 1h 30m

      Morning Session II: Application, discussion, hands-on practice

      Speakers: Charles Ndegwa, Patrick Gikunda
    • 12:30 PM 2:00 PM
      Lunch 1h 30m
    • 2:00 PM 3:30 PM
      Practice with PyTorch 1h 30m

      Afternoon Session I: More hands-on: coding lab, group work, demos

      Speakers: Isabelle Rocamora (ISTerre Grenoble), Patrick Gikunda
    • 3:30 PM 4:00 PM
      Coffee and tea break 30m
    • 4:00 PM 5:30 PM
      Practice with PyTorch 1h 30m

      Afternoon Session II: Light content: Q&A, recap, open discussion

      Speakers: Isabelle Rocamora (ISTerre Grenoble), Patrick Gikunda
    • 5:30 PM 6:15 PM
      The Engineering and Science Complex of the University of Nairobi 45m

      Marc Zolver

    • 8:00 AM 8:30 AM
      Daily registration 30m
    • 8:30 AM 10:30 AM
      Practice with PyTorch 2h

      Morning Session I: High-focus content (theory-heavy or technical demos)

      Speakers: Patrick Gikunda, Isabelle Rocamora (ISTerre Grenoble)
    • 10:30 AM 11:00 AM
      Coffee and tea break 30m
    • 11:00 AM 12:30 PM
      Practice with PyTorch 1h 30m

      Morning Session II: Application, discussion, hands-on practice

      Speakers: Patrick Gikunda, Isabelle Rocamora (ISTerre Grenoble)
    • 12:30 PM 2:00 PM
      Lunch 1h 30m
    • 2:00 PM 3:30 PM
      Convolutional Neural Networks 1h 30m

      Afternoon Session I: More hands-on: coding lab, group work, demos

      Speakers: Charles Ndegwa, Isabelle Rocamora (ISTerre Grenoble)
    • 3:30 PM 4:00 PM
      Coffee and tea break 30m
    • 4:00 PM 5:30 PM
      Convolutional Neural Networks 1h 30m

      Afternoon Session II: Light content: Q&A, recap, open discussion

      Speakers: Charles Ndegwa, Isabelle Rocamora (ISTerre Grenoble)
    • 5:30 PM 6:15 PM
      Demo from a recent PhD work: An Intelligent Traffic Light Control Model based on Deep Reinforcement Learning Algorithm 45m

      Omina James Adunya

    • 8:00 AM 8:30 AM
      Daily registration 30m
    • 8:30 AM 10:30 AM
      Convolutional Neural Networks 2h

      Morning Session I: High-focus content (theory-heavy or technical demos)

      Speakers: Charles Ndegwa, Isabelle Rocamora (ISTerre Grenoble)
    • 10:30 AM 11:00 AM
      Coffee and tea break 30m
    • 11:00 AM 12:30 PM
      Convolutional Neural Networks 1h 30m

      Morning Session II: Application, discussion, hands-on practice

      Speakers: Charles Ndegwa, Isabelle Rocamora (ISTerre Grenoble)
    • 12:30 PM 2:00 PM
      Lunch 1h 30m
    • 2:00 PM 3:30 PM
      Graph Neural Networks 1h 30m

      Afternoon Session I: More hands-on: coding lab, group work, demos

      Speaker: Patrick Gikunda
    • 3:30 PM 4:00 PM
      Coffee and tea break 30m
    • 4:00 PM 5:30 PM
      Graph Neural Networks 1h 30m

      Afternoon Session II: Light content: Q&A, recap, open discussion

      Speaker: Patrick Gikunda
    • 5:30 PM 7:00 PM
      Free Time / Networking / Group work assignments 1h 30m
    • 8:00 AM 8:30 AM
      Daily registration 30m
    • 8:30 AM 10:30 AM
      Generative Models & Transformers 2h

      Morning Session I: High-focus content (theory-heavy or technical demos)

      Speaker: Patrick Gikunda
    • 10:30 AM 11:00 AM
      Coffee and tea break 30m
    • 11:00 AM 12:30 PM
      Generative Models & Transformers 1h 30m

      Morning Session II: Application, discussion, hands-on practice

      Speaker: Patrick Gikunda
    • 12:30 PM 2:00 PM
      Lunch 1h 30m
    • 2:00 PM 3:30 PM
      Convolutional Neural Networks 1h 30m

      Afternoon Session I: More hands-on: coding lab, group work, demos

      Speakers: Charles Ndegwa, Isabelle Rocamora (ISTerre Grenoble)
    • 3:30 PM 4:00 PM
      Coffee and tea break 30m
    • 4:00 PM 5:30 PM
      Convolutional Neural Networks 1h 30m

      Afternoon Session II: Light content: Q&A, recap, open discussion

      Speakers: Charles Ndegwa, Isabelle Rocamora (ISTerre Grenoble)
    • 5:30 PM 6:15 PM
      Demo from a recent PhD work: Enhanced Malaria Preparedness through Climate Data-Driven Small Area Prediction of Future Malaria Case Burden 45m

      Dr. Chrisgone Adede

    • 8:00 AM 8:30 AM
      Daily registration 30m
    • 8:30 AM 10:30 AM
      Convolutional Neural Networks 2h

      Morning Session I: High-focus content (theory-heavy or technical demos)

      Speakers: Charles Ndegwa, Isabelle Rocamora (ISTerre Grenoble)
    • 10:30 AM 11:00 AM
      Coffee and tea break 30m
    • 11:00 AM 12:30 PM
      Convolutional Neural Networks 1h 30m

      Morning Session II: Application, discussion, hands-on practice

      Speakers: Charles Ndegwa, Isabelle Rocamora (ISTerre Grenoble)
    • 12:30 PM 2:00 PM
      Lunch 1h 30m
    • 2:00 PM 3:30 PM
      Boosted Decision Trees 1h 30m

      Afternoon Session I: More hands-on: coding lab, group work, demos

      Speakers: Charles Ndegwa, Luca Cadamuro (IJCLab)
    • 3:30 PM 4:00 PM
      Coffee and tea break 30m
    • 4:00 PM 5:30 PM
      Boosted Decision Trees 1h 30m

      Afternoon Session II: Light content: Q&A, recap, open discussion

      Speakers: Charles Ndegwa, Luca Cadamuro (IJCLab)
    • 5:30 PM 7:00 PM
      Free Time / Networking / Group work assignments 1h 30m
    • 8:00 AM 7:00 PM
      Free time 11h
    • 8:00 AM 8:30 AM
      Daily registration 30m
    • 8:30 AM 12:30 PM
      Common Dataset Hands-On Exercise 4h
      Speakers: Charles Ndegwa, Isabelle Rocamora (ISTerre Grenoble), Luca Cadamuro (IJCLab), Patrick Gikunda
    • 12:30 PM 2:00 PM
      Lunch 1h 30m
    • 2:00 PM 6:00 PM
      Common Dataset Hands-On Exercise 4h
      Speakers: Charles Ndegwa, Isabelle Rocamora (ISTerre Grenoble), Luca Cadamuro (IJCLab), Patrick Gikunda
    • 7:00 PM 9:00 PM
      Gala dinner at the United Kenya Club 2h

      https://maps.app.goo.gl/HgVpK8FgqmHbjJs3A

    • 8:00 AM 8:30 AM
      Daily Registration 30m
    • 8:30 AM 12:30 PM
      Common Dataset Hands-On Exercise 4h
      Speakers: Charles Ndegwa, Isabelle Rocamora (ISTerre Grenoble), Luca Cadamuro (IJCLab), Patrick Gikunda
    • 12:30 PM 2:00 PM
      Lunch 1h 30m
    • 2:00 PM 3:30 PM
      Discussion on common Dataset Hands-On Exercise 1h 30m
      Speakers: Charles Ndegwa, Isabelle Rocamora (ISTerre Grenoble), Luca Cadamuro (IJCLab), Patrick Gikunda
    • 3:30 PM 4:00 PM
      Coffee and tea break 30m
    • 4:00 PM 5:00 PM
      Closing session 1h