SCL Seminar by Andreja Stojic
SCL seminar of the Center for the Study of Complex Systems, will be held on Thursday, 20 September 2018 at 14:00 in the library reading room “Dr. Dragan Popović" of the Institute of Physics Belgrade. The talk entitled
"Explainable machine learning"
will be given by Dr. Andreja Stojić (Environmental Physics Laboratory, Institute of Physics Belgrade).
Abstract of the talk:
Over recent years, the success in the application of artificial intelligence implemented in machine learning, supported by the great availability of high-dimensional data, has become evident in all fields of science. Nevertheless, understanding and correctly interpreting complex models for predicting natural and social phenomena, such as random forests, deep neural networks or an extreme gradient boosting, can be challenging. The ability to accurately interpret a model’s prediction, comprehend causality and features that drive prediction, supports deeper understanding of the process being modeled. Therefore, it is critical that researchers gain insight into the way such models arrive at their predictions. This seminar will cover some basic concepts of machine learning and an a posteriori explanation framework based on consistent, locally accurate, individualized feature attribution methods, aimed at shedding light on problems where human intuition and domain knowledge are often limited.
"Explainable machine learning"
will be given by Dr. Andreja Stojić (Environmental Physics Laboratory, Institute of Physics Belgrade).
Abstract of the talk:
Over recent years, the success in the application of artificial intelligence implemented in machine learning, supported by the great availability of high-dimensional data, has become evident in all fields of science. Nevertheless, understanding and correctly interpreting complex models for predicting natural and social phenomena, such as random forests, deep neural networks or an extreme gradient boosting, can be challenging. The ability to accurately interpret a model’s prediction, comprehend causality and features that drive prediction, supports deeper understanding of the process being modeled. Therefore, it is critical that researchers gain insight into the way such models arrive at their predictions. This seminar will cover some basic concepts of machine learning and an a posteriori explanation framework based on consistent, locally accurate, individualized feature attribution methods, aimed at shedding light on problems where human intuition and domain knowledge are often limited.
