East-African School for Young Researchers on Advanced Machine Learning Techniques 2027

13 janv. 2027, 08:00 → 27 janv. 2027, 20:00 Africa/Nairobi

University of Nairobi, Chiromo Campus

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

We are delighted to inform you that the second edition of the East-African School for Young Researchers on Advanced Machine Learning Techniques (EASY-ML) will be held at University of Nairobi, Chiromo Campus, from January 13th to 27nd, 2027. The call for applications will be opened in October 2026.

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 will be 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, a workshop on how to build a startup, and a poster session for participants to present their work.

Facilitators come from France 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-2027 

To ensure EASY-ML has the broadest possible impact, we regret to announce that participants from the first edition of EASY-ML are not eligible for EASY-ML 2027. Visit the EASY-ML 2025 website:  https://indico.in2p3.fr/e/easy-ml

 

mer. 13 janvier

08:00 Registration

1 Welcome address

Orateur: Ian Kaniu (University of Nairobi)

2 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

10:30 Coffee and tea break

3 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

12:30 Lunch

4 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

15:30 Coffee and tea break

5 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

17:30 Introduction to the project session

jeu. 14 janvier

07:30 Daily registration

6 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

7 Official opening

10:30 Coffee and tea break

8 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

12:30 Lunch

9 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

15:30 Coffee and tea break

10 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

17:30 Free Time / Networking / Group work assignments

ven. 15 janvier

08:00 Daily registration

11 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

10:30 Coffee and tea break

12 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

12:30 Lunch

13 Workshop: how to found a start-up? (part 1)

15:30 Coffee and tea break

14 Workshop: how to found a start-up? (part 2)

15 Poster Session

sam. 16 janvier

08:00 Daily registration

16 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

10:30 Coffee and tea break

17 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

12:30 Lunch

18 Ethics in AI

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

15:30 Coffee and tea break

19 Debate

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

17:30 Free Time / Networking / Group work assignments

dim. 17 janvier

08:00 Excursion

lun. 18 janvier

08:00 Daily registration

20 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

10:30 Coffee and tea break

21 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

12:30 Lunch

22 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

15:30 Coffee and tea break

23 Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics)

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

17:30 AI in business

mar. 19 janvier

08:00 Daily registration

24 Unsupervised Learning

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

10:30 Coffee and tea break

25 Unsupervised Learning

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

12:30 Lunch

26 Practice with PyTorch

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

15:30 Coffee and tea break

27 Practice with PyTorch

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

17:30 Free Time / Networking / Group work assignments

mer. 20 janvier

08:00 Daily registration

28 Unsupervised learning

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

10:30 Coffee and tea break

29 Unsupervised learning

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

12:30 Lunch

30 Visit of a Data Centre

17:30 Free Time / Networking / Group work assignments

jeu. 21 janvier

08:00 Daily registration

31 Practice with PyTorch

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

Orateurs: Dr Charles Ndegwa (CentraleSupélec / LISN / CNRS, Paris-Saclay University, Gif-sur-Yvette, France), Isabelle Rocamora (ISTerre Grenoble)

10:30 Coffee and tea break

32 Practice with Pytorch

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

12:30 Lunch

33 Convolutional Neural Networks

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

15:30 Coffee and tea break

34 Convolutional Neural Networks

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

17:30 Demo from a recent PhD work

ven. 22 janvier

08:00 Daily registration

35 Convolutional Neural Networks

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

10:30 Coffee and tea break

36 Convolutional Neural Networks

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

12:30 Lunch

37 Graph Neural Networks

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

15:30 Coffee and tea break

38 Graph Neural Networks

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

17:30 Free Time / Networking / Group work assignments

Dr. Chrisgone Adede

sam. 23 janvier

08:00 Daily registration

39 Generative Models & Transformers

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

10:30 Coffee and tea break

40 Generative Models & Transformers

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

12:30 Lunch

41 Convolutional Neural Networks

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

15:30 Coffee and tea break

42 Convolutional Neural Networks

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

17:30 The Engineering and Science Complex @ UoN

dim. 24 janvier

08:30 Free time

lun. 25 janvier

08:00 Daily registration

43 Convolutional Neural Networks

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

10:30 Coffee and tea break

44 Convolutional Neural Networks

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

12:30 Lunch

45 Boosted Decision Trees

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

15:30 Coffee and tea break

46 Boosted Decision Trees

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

17:30 Cocktail

mar. 26 janvier

08:00 Daily registration

47 Common Dataset Hands-On Exercise

Orateurs: Dr Charles Ndegwa (CentraleSupélec / LISN / CNRS, Paris-Saclay University, Gif-sur-Yvette, France), Isabelle Rocamora (ISTerre Grenoble), Luca Cadamuro (IJCLab), Patrick Gikunda

12:30 Lunch

48 Common Dataset Hands-On Exercise

Orateurs: Dr Charles Ndegwa (CentraleSupélec / LISN / CNRS, Paris-Saclay University, Gif-sur-Yvette, France), Isabelle Rocamora (ISTerre Grenoble), Luca Cadamuro (IJCLab), Patrick Gikunda

18:00 Gala dinner at the United Kenya Club

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

mer. 27 janvier

08:00 Daily registration

49 Common Dataset Hands-On Exercise

10:30 Coffee and tea break

50 Preparation of the presentations on the hands-on exercises

12:30 Lunch

51 Presentation on Hands-On Exercise

52 Closing session