In many binary classification problems, especially in domains with highly unbalanced problems (such as the medical domain and rare event detection), we need to make sure our model does not […]
Explainable AI in Healthcare
Transparency is of utmost importance when AI is applied to high stake decision problems where additional information on the underlying process beyond the output of the model may be required. […]
Fairness in AI
In partnership with Data Science for Social Good Portugal, we are launching a series of webinars in AI topics related to social good. The second talk was by Francisca Morgado, […]
Experiment Management and Reproducible Research
In this tutorial, we will discuss how can we achieve reproducible data pipelines and research while keeping track of the experiments that lead to production Machine Learning models. We will […]
Who is tampering your meters? Fraud detection in Utilities
Meter tampering is a common threat to the business side of the utility services as well as a public security threat, incurring in uncontrolled tweaks that may increase the risk […]
Embedding Domain Knowledge for Estimating Customer Lifetime Value
As part of the rise of Deep Neural Networks in the ML community, we have observed an increasing fit-predict approach, where AI practitioners don’t take the time to think about […]
Appendix: Embedding Domain Knowledge for Estimating Customer Lifetime Value
This is an appendix to a blogpost previously published on Embedding Domain Knowledge for Estimating Customer Lifetime Value. We will describe some alternatives we considered for solving the proposed problem, […]
Objectively Estimating Data Quality: A Weakly Supervised Approach
In Artificial Intelligence, it is important to have some measure of the quality of the data we are trying to use. For instance, if we want to classify a […]