After testing 100 hyperparameter candidates, the best accuracy was 92.5%. But is that value really "optimal"? In reality, it ...

Introduction I first started working with machine learning in earnest when I took on a small project to classify internal inquiry logs. I managed to get scikit-learn code running by piecing together ...

This page provides the python code for preferential Bayesian optimization (BO), which includes experiments in [1]. We implement Gibbs sampling-based preferential Gaussian process regression (GPR) and ...

Abstract: Testing controllers in safety-critical systems is vital for ensuring their safety and preventing catastrophic failures. In this paper, we address the falsification problem within closed-loop ...

David Nnamdi, SPE, is a senior data scientist at Intuit. He has expertise in statistical analysis, machine learning (ML), distributed computing, and convex optimization, leveraging analytics to drive ...

You don't need a rigorous math or computer science degree to get into data science. But you do need to understand the mathematical concepts behind the algorithms and analyses you'll use daily. But why ...

Anomaly response in aerospace systems increasingly relies on multi-model analysis in digital twins to replicate the system’s behaviors and inform decisions. However, computer model calibration methods ...

Contamination of treated drinking water is a critical public health and safety concern. In this study, a multi-objective Bayesian optimization (MOBO) framework is proposed to optimize operational ...

In 2025, AI engineers have become essential as AI transforms industries worldwide. Python programmers have a strong starting point, but transitioning to AI engineering demands expanding your expertise ...