This hands-on guide offers clear explanations of fuzzy logic along with practical applications and real-world examples. Written by an award-winning engineer, “Fuzzy Logic: Applications in Artificial ...

Inductive logic programming (ILP) and machine learning together represent a powerful synthesis of symbolic reasoning and statistical inference. ILP focuses on deriving interpretable logic rules from ...

The field of interpretability investigates what machine learning (ML) models are learning from training datasets, the causes and effects of changes within a model, and the justifications behind its ...

The intersection of machine learning and mathematical logic — spanning computer science, pure mathematics, and statistics — has catalyzed recent advances in artificial intelligence and deep learning ...

AI, Machine Learning & Robotics research at Drexel University's College of Computing & Informatics (CCI) explores algorithms, mathematics, and applications of artificial intelligence (AI) through ...

Machine learning, with its ability to analyze large datasets and identify patterns, is particularly well-suited to address the challenges presented by the vast and complex data generated in ...

Gain a deeper understanding of artificial intelligence with Machine Learning Fundamentals: Principles and Applications. This course explores core concepts and practical uses of supervised and ...

In recent years, machine learning (ML) algorithms have proved themselves to be remarkably useful in helping people deal with different tasks: data classification and clustering, pattern revealing, ...

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...

Machine learning is based on the idea that a system can learn to perform a task without being explicitly programmed. Machine learning has a wide range of applications in the finance, healthcare, ...