First School on Data Science and Machine Learning
Machine Learning (ML) concepts will drive some critical changes in our society during the next decades. The cross-cutting character of the 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 it will guide the creation of new public policies. In that, sense, 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.
The school is organized together with the International Centre for Theoretical Physics – South American Institute for Fundamental Research (ICTP-SAIFR).
Application deadline: October 13, 2019
- Introduction to Machine Learning (Presentation Materials)
- Neural Networks (Presentation Materials)
- Convolutional Neural Networks (Presentation Materials)
- Generative Models (Presentation Materials)
- Natural Language Processing (Presentation Materials)
- Reinforcement Learning (Presentation Materials)
- Nathan Berkovits (ICTP-SAIFR/IFT-Unesp, Brazil)
- Raphael Cobe (NCC-Unesp/AI2, Brazil)
- Sergio F. Novaes (Unesp/AI2, Brazil)
- Maria Spiropulu (Californa Institute of Technology, USA)
- Thiago Tomei (NCC-Unesp/AI2, Brazil)
- Reinaldo A. C. Bianchi (FEI, Brazil)
- André Carlos Ponce de Leon Ferreira de Carvalho (ICMC – USP, Brazil)
- Anna Helena Reali Costa (EP-USP, Brazil)
- Alexandre Xavier Falcão (IC-Unicamp, Brazil)
- Marcelo Finger (IME-USP, Brazil)
- João Paulo Papa (FC-Unesp, Brazil)
- Felipe Leno da Silva (EP-USP, Brazil)