Sort by
Refine Your Search
-
Category
-
Country
-
Program
-
Field
-
testing approaches that can be used to verify that machine learning models are not biased. Required knowledge Software engineering, software testing, statistics, machine learning
-
. Headquartered at Monash University, CEVAW comprises 14 chief investigators at seven Australian institutions, 17 partner investigators at 15 institutions worldwide, 33 partner organisations and over 100 HDR
-
of statistical signal processing, inference, machine learning and dynamical systems theory to develop new semi-analtyical filtering approaches for state and parameter estimation to infer neurophysiological
-
This project aims to explore techniques for characterising the complexity of statistical models. By complexity we refer to the ability of a model to learn patterns, and to potentially generalise
-
of APR techniques and make them more user-friendly. Required knowledge Software Engineering Artificial Intelligence Statistics
-
of the following: Statistics, Machine Learning, Deep Learning, Multivariate Modelling. As well as an interest in personal development, psychology, sociology, economics or something else important in life.
-
The brain is a complex machine and brain function remains yet to be fully understood. This project works at the intersection of dynamical modelling, statistical signal processing, statistical
-
models that can forecast the likely outcomes of current practices. The project aims to develop cutting-edge machine learning and statistical risk prediction techniques to predict each short-term, long-term
-
intelligence techniques (e.g., Deep Learning, Statistics, ML, Optimization) in order to (1) understand the nature of critical software defects like vulnerabilities; (2) predict; (3) highlight vulnerable code; (4
-
. (2005), ``Statistical and Inductive Inference by Minimum Message Length '', Springer (Link to the preface [and p vi , also here ]) Wallace, C.S. and D.M. Boulton (1968), ``An information measure for