Nettsider med emneord «Machine Learning» - Side 4

Classification models are often used to make decisions that affect humans: whether to approve a loan application, extend a job offer, or provide insurance. In such applications, it is desirable for the individuals to not only knowing the results but also have the ability to change the decisions of the model. For example, when a person is denied a loan by a credit scoring model, in addition to know why he/she can not received the loan, it is meaningful for the person to know what he/she can do to influence the decision, i.e. what are the input variables that, if values are changed, can alter the decision of the model. Otherwise, without this information, he/she will be denied the loan as long as the model is deployed, and – more importantly – will lack agency over a decision that affects their livelihood.

Insights is a 4-year Marie Sklodowska-Curie Innovative Training Networks project for the career development of 12 Early Stage Researchers (ESRs) at 10 partner institutions across Europe. INSIGHTS is focused on applying the latest advances in statistics, and in particular machine learning, to particle physics.

Two new papers from SANT were presented at the NoDaLiDa conference.

In the field of robotics, creating grippers that can perform tasks similar to the capabilities of a human hand, is still a challenge yet to be solved. 

Water flow on, in and under glaciers still remains a poorly understood system. Water can flow over the surface of glaciers, as well as through channels inside and under the ice. The water thereby has strong influence both on ice melt and glacier dynamics, making glacial hydrology an important field of study.

João Gama, Associate Professor at the University of Porto, talks about the limitations of current machine learning and data mining algorithms in dealing with real-time data.

Tuyen Trung Truong talks about the gradient descent method, and why such a simple method performs so well in practice.

Christos Dimitrakakis talks about the challenge of identity protection.