Welcome address

• Ian Kaniu (University of Nairobi)

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)

Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics) Morning Session II: Application, discussion, hands-on practice

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

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

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)

Official opening

Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics) Morning Session II: Application, discussion, hands-on practice

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

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

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)

Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics) Morning Session II: Application, discussion, hands-on practice

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

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

Poster Session

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)

Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics) Morning Session II: Application, discussion, hands-on practice

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

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

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)

Introduction to Machine learning (Python, linear regression, classification, neural networks, forward propagation, backward propagation, ethics) Morning Session II: Application, discussion, hands-on practice

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

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

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

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

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

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

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

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

Visit of a Data Centre

Practice with PyTorch

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

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

Practice with Pytorch Morning Session II: Application, discussion, hands-on practice

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

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

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

Convolutional Neural Networks Morning Session II: Application, discussion, hands-on practiceCon

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

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

Generative Models & Transformers Morning Session I: High-focus content (theory-heavy or technical demos)

Generative Models & Transformers Morning Session II: Application, discussion, hands-on practice

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

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

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

Convolutional Neural Networks Morning Session II: Application, discussion, hands-on practice

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

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

Common Dataset Hands-On Exercise

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

Common Dataset Hands-On Exercise

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

Common Dataset Hands-On Exercise

Preparation of the presentations on the hands-on exercises

Presentation on Hands-On Exercise

Closing session