Second School on Data Science and Machine Learning

Machine Learning (ML) concepts will drive critical changes in our society during the next decades. The cross-cutting character of ML tools can be used to attack a wide variety of problems that could improve our lives, for instance, designing solutions for medical diagnosis, providing smart assistance to the disabled and the elderly, and building solutions for public safety. The positive impact of these applications is expected to raise awareness on the subject and guide the creation of new public policies. For this reason, the training of people on the most advanced topics in ML is very important for the success and development of the area.

The School on Data Science and Machine Learning has the goal of teaching participants about modern machine learning techniques, their strengths and shortcomings, and how to apply them in different contexts. The school is targeted particularly at senior PhD students, working towards the completion of their thesis projects, as well as young postdocs.

The school participants will learn the formalism of machine learning, starting from an introductory level and going through more advanced topics like computer vision, sequential and recursive learning, anomaly and outlier detectors, and generative models. The theoretical lectures will be mixed with a set of hands-on sessions where participants will be able to apply the concepts to solving real-world problems.

There is no registration fee and limited funds are available for travel and local expenses.

Topics:

• Introduction to Machine Learning
• Neural Networks
• Convolutional Neural Networks
• Generative Models
• Reinforcement Learning
• Natural Language Processing

Organizers:

• Raphael Cobe (NCC-UNESP/AI2, Brazil)
• Sergio F. Novaes (UNESP/AI2, Brazil) 
• Thiago Tomei (NCC-UNESP/AI2, Brazil) 

List of participants: Updated on December 07, 2023.

Survey: Here

Lecturers:

• Raphael Mendes de Oliveira Cóbe (NCC – Unesp, Brazil): Introduction to Machine Learning Models and Neural Networks
• Alexandre Xavier Falcão (IC – Unicamp, Brazil): Convolutional Neural Networks
• Luciano Oliveira (UFBA, Brazil): Generative Models
• Marcelo Finger (IME-USP, Brazil): Natural Language Processing
• Felipe Fernandes Fanchini (FC-Bauru-Unesp, Brazil): Quantum Machine Learning

Announcement:

The application is now closed

Download PDF version: HERE

December 7, 2023

1. Flora Medeiros Sauerbronn (Federal University of Santa Catarina): Saving dolphins through AI: Creating a tool for IBAMA audits
2. Luciana Erika Yaginuma (Instituto Oceanográfico of Universidade de São Paulolu): Using machine learning techniques to predict benthic communties spatial distribution
3. Sarah Peixoto Rodrigues Freire (Ilum School of Science- Brazilian Center for Research in Energy and Materials(CNPEM)): Inverse Project of New Bioactive Glasses
4. Luiz Felipe Demétrio (Universidade Estadual de Londrina): The Era of Precision Cosmology: probing the Primordial Universe
5. Romana Petry (Universidade Federal do ABC): When theoretical physics meets material science and environmental nanotoxicology
6. Adriana Canedo Miranda (PUCRS ): Large-scale proteogenomics characterization of the M. tuberculosis hidden proteome
7. Raul de Palma Aristides (IFT – UNESP): Inferring the connectivity of networks with Kalman Filter
8. Denise Cammarota (IFT UNESP): Data in Epidemic Modeling
9. Thales Fernandes (Butantan Institute): Protein Structure Modelling with Deep Learning
10. André Martinez (Unicamp): Connectivity of Atlantic Forest fragments through seed dispersal
11. Ignacio Alcántara (Biostatistics Unit, Public Health Department, Universidad de la Republica): Developing Metrics for Comparing the Epidemiology of Veterinary Drug Use in Cattle Parasite Control: An Application of the ATCvet System in One Health

Format: 4 min talk with slide presentation

2023-12-04

• 09:30 - Raphael Mendes de Oliveira Cóbe (NCC – Unesp, Brazil): Introduction to Machine Learning Models and Neural Networks - Class 1
  • Attachment
• 11:30 - Raphael Mendes de Oliveira Cóbe (NCC – Unesp, Brazil): Introduction to Machine Learning Models and Neural Networks - Class 2
  • Attachment
• 14:30 - Raphael Mendes de Oliveira Cóbe (NCC – Unesp, Brazil): Introduction to Machine Learning Models and Neural Networks - Class 3
  • Attachment
• 16:30 - Raphael Mendes de Oliveira Cóbe (NCC – Unesp, Brazil): Introduction to Machine Learning Models and Neural Networks - Class 4
  • Attachment

2023-12-05

• 09:30 - Alexandre Xavier Falcão (IC – Unicamp, Brazil): Convolutional Neural Networks - Class 1
  • Attachment
• 11:30 - Alexandre Xavier Falcão (IC – Unicamp, Brazil): Convolutional Neural Networks - Class 2
  • Attachment
• 14:30 - Alexandre Xavier Falcão (IC – Unicamp, Brazil): Convolutional Neural Networks - Class 3
  • Attachment
• 16:30 - Alexandre Xavier Falcão (IC – Unicamp, Brazil): Convolutional Neural Networks - Class 4
  • Attachment

2023-12-06

• 09:30 - Luciano Oliveira (UFBA, Brazil): Generative Models - Class 1
  • Attachment
• 11:30 - Luciano Oliveira (UFBA, Brazil): Generative Models - Class 2
  • Attachment
• 14:30 - Luciano Oliveira (UFBA, Brazil): Generative Models - Class 3
  • Attachment
• 16:30 - Luciano Oliveira (UFBA, Brazil): Generative Models - Class 4
  • Attachment

2023-12-07

• 09:30 - Marcelo Finger (IME-USP, Brazil): Natural Language Processing - Class 1
  • Attachment
• 11:30 - Marcelo Finger (IME-USP, Brazil): Natural Language Processing - Class 2
  • Attachment
• 14:30 - Marcelo Finger (IME-USP, Brazil): Natural Language Processing - Class 4
  • Attachment
• 16:30 - Marcelo Finger (IME-USP, Brazil): Natural Language Processing - Class 4
  • Attachment

2023-12-08

• 09:30 - Felipe Fernandes Fanchini (FC-Bauru-Unesp, Brazil): Quantum Machine Learning - Class 1
• 11:30 - Felipe Fernandes Fanchini (FC-Bauru-Unesp, Brazil): Quantum Machine Learning - Class 2
• 14:30 - Felipe Fernandes Fanchini (FC-Bauru-Unesp, Brazil): Quantum Machine Learning - Class 3
• 16:30 - Felipe Fernandes Fanchini (FC-Bauru-Unesp, Brazil): Quantum Machine Learning - Class 4

BOARDING PASS: All participants, whose travel has been provided or will be reimbursed by ICTP-SAIFR, should bring the boarding pass upon registration. The return boarding pass (PDF, if online check-in, scan or picture, if physical) should be sent by e-mail to secretary@ictp-saifr.org

COVID-19: Brazilians and foreigners no longer have to present proof of vaccination before entering the country.

Visa information: Nationals from several countries in Latin America and Europe are exempt from tourist visa. Nationals from Australia, Canada and USA need a tourist visa starting from January 10, 2024. Please check here which nationals need a tourist visa to enter Brazil.

Accommodation: Participants, whose accommodation will be provided by the institute, will stay at The Universe Flat. Hotel recommendations are available here.

How to reach the Institute: The school will be held at ICTP South American Institute, located at IFT-UNESP, which is across the street from a major bus and subway terminal (Terminal Barra Funda). The address which is closer to the entrance of the IFT-UNESP building is R. Jornalista Aloysio Biondi, 120 – Barra Funda, São Paulo. The easiest way to reach us is by subway or bus, please find instructions here.

Poster presentation: Participants who are presenting a poster MUST BRING A PRINTED BANNER . The banner size should be at most 1 m (width) x 1,5 m (length). We do not accept A4 or A3 paper.