- Ricardo Galante, SAS Portugal
- FCUL – Campo Grande – Bloco C6 Piso 4 – Sala: 6.4.31 – 09:00h – 13:00h
- Sábado, 18 de Maio de 2019 e Sábado, 25 de Maio de 2019
Date:
18 de Maio de 2019 – 9h às 13h
25 de Maio de 2019 – 9h às 13h – 14h30 às 16h30
Local:
Faculdade de Ciências da Universidade de Lisboa, Portugal
Lecturer:
Ricardo Galante, Senior Analytics Costumer Advisor at SAS Portugal. https://www.linkedin.com/in/ricardogalante/
Course Summary:
Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development.
During this course, we will see a few algorithms widely used and some examples of applications of advanced Analytics and Machine Learning using the SAS solution.
Program:
• Descriptive Statistics
• Cluster Analysis
• Decision Tree
• Logistic Regression
• Gradient Boosting
• Random Forest
• Neural Network
• Forecasting
• Text Analytics
• Computer Vision
Registration: Aberta para alunos dos mestrados em MAEG, Bioestatística, EIO e Ciência de Dados, ou para convidados, no valor de 15€, incluindo coffee breaks e almoço no dia 25 de maio, servido no Clube Ciências (C6) . A inscrição no curso deve ser realizada enviando e-mail até 14 de maio para ceaul@fc.ul.pt. O número de vagas é de 30.
Necessary: Portátil (Bateria completamente cheia é extremamente recomendado)