Using Machine Learning to explain causality: no more spurious correlations!
Language
Italian
Level
Advanced
Length
32 minutes
Type
online conference
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About
Algorithms • March 2021
Using Machine Learning to explain causality: no more spurious correlations!
Machine Learning has long been used for classification, regression and ranking tasks, pushing the boundaries of performance with more and more complex algorithms. Surprisingly, a much lower attention has been put on using ML to answer a different question that is frequent for any company: what's been the actual impact of a given action (like a campaign) on a target metric? Leaving aside the traditional treatment-control approach, not always applicable, we'll see how Causal Machine Learning actually works and how to implement it effectively.
About speakers
Alberto Danese
Head of Data ScienceNexi
Alberto is the Head of Data Science at Nexi, the Italian leader in digital payments. After graduating at Politecnico di Milano, he started working in IT security, before moving to data science and machine learning. In the last years spent in credit information and digital payments, he has learned the importance of combining technology, business and a scientific approach in order to achieve tangible results with data. He is passionate about Kaggle, Google-owned platform for machine learning competitions with millions of users, where he's the only Italian in the top tier (Grandmaster).
Marta Toschi
Senior Data ScientistNexi
Marta is a Data Scientist at Nexi, the Italian leader in digital payments. After graduating as a Mathematical Engineer at Politecnico di Milano, she started working in a consultant company, getting in touch with data science and machine learning world applied to multiple areas and learning the power of using data analytics to improve business decision-making process.
Details
Language
Italian
Level
Advanced
Length
32 minutes
Type
online conference
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