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4th School on Data Science and Machine Learning

November 16-21, 2025
ICTP-SAIFR, São Paulo, Brazil

About

The 4th School on Data Science and Machine Learning invites ambitious researchers ready to harness the transformative power of advanced artificial intelligence. Building on our successful four-year legacy, this year's program features state-of-the-art topics reflecting the rapid evolution of AI in 2025.

Machine Learning is revolutionizing every sector of society — from breakthrough medical diagnostics to intelligent systems supporting vulnerable populations to innovative public safety solutions. These advancements aren't just technological achievements; they are catalysts for new public policies and social frameworks.

Target Audience

Our program is tailored for:

  • Advanced PhD candidates finalizing research
  • Early-career postdoctoral researchers
  • Professionals seeking to integrate cutting-edge AI into their work
  • Researchers from diverse backgrounds looking to apply AI to their disciplines

Organizers

  • Raphael Cobe (NCC-UNESP/AI2, Brazil)
  • Tommaso Dorigo (INFN-Padova, Italy)
  • Sergio F. Novaes (NCC-UNESP/AI2, Brazil)
  • Thiago Tomei (NCC-UNESP/AI2, Brazil)

Repository Structure

dsml2025/
├── README.md                 # This file
├── docs/                     # Documentation and additional information
├── lectures/                 # Lecture materials organized by day
├── exercises/                # Hands-on exercises and tutorials
├── resources/                # Additional learning resources
└── schedule/                 # Detailed schedule and logistics

Schedule Overview

Sunday: Toolbox Kickoff

  • Pandas - Data Wrangling 101
  • Matplotlib - Data Visualization
  • PyTorch - Tensors & Computation

Monday: Foundations of Neural Networks

  • Statistical Methods for Data Analysis
  • Neural Networks 101

Tuesday: Going Deeper with Neural Networks

  • From Single to Multi-Layer Networks
  • Backpropagation Explained
  • Convolutional Neural Networks (CNNs) I

Wednesday: Specialized Architectures

  • Convolutional Neural Networks (CNNs) II
  • Recurrent Neural Networks (RNNs)
  • Transformers and Large Language Models

Thursday: Applications and Domain-Specific Uses

  • AI in natural sciences
  • AI for the Climate Emergency
  • AI in humanities and social sciences
  • The Role of HPC in AI

Friday: Frontiers and Future Directions

  • Foundation Models
  • Physics-enhanced Machine Learning
  • Explainable AI
  • Panel discussion: "Ethics in AI"

Venue

Location: IFT-UNESP
Address: R. Jornalista Aloysio Biondi, 120 - Barra Funda, São Paulo

For arrival instructions, visit: How to reach us

Poster Presentation

All participants are required to bring a research poster to be presented during coffee breaks and lunch intervals. This is an opportunity to showcase your work, receive feedback, and engage in meaningful discussions.

Requirements:

  • Participants MUST BRING A PRINTED BANNER
  • Banner size: at most 1 m (width) x 1.5 m (length)
  • A4 or A3 paper is NOT accepted

Contact

ICTP South American Institute for Fundamental Research
IFT-UNESP (1º andar), Rua Dr. Bento Teobaldo Ferraz 271, Bloco 2 - Barra Funda
01140-070 São Paulo, SP Brazil
Phone: +55 (11) 3393 7839
Email: secretary@ictp-saifr.org

Official Website

For more information, visit: https://www.ictp-saifr.org/dsml2025/

License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

You are free to:

  • Share - copy and redistribute the material in any medium or format
  • Adapt - remix, transform, and build upon the material for any purpose, even commercially

Under the following terms:

  • Attribution - You must give appropriate credit to ICTP-SAIFR and the 4th School on Data Science and Machine Learning, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

Individual lecture materials and content may be subject to the original authors' copyright. Please refer to specific files for their respective licenses.

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