The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You’ll then learn all you need to know about the main machine learning ...

During his career, he has developed a solid background in artificial intelligence, graph theory, and machine learning, with a focus on the biomedical field. He is currently a senior data scientist in ...

How can graph theory and data mining be integrated with bioengineering to study biological systems? To gather further insights into the intersection of machine learning and bioengineering, we welcome ...

Choosing the right chart or graph for your machine learning results can make a big difference in how your audience understands and appreciates your work. In this article, you will learn about some ...

This framework formalizes reasoning as a structured mapping M: T→(G, P, A), where tasks generate knowledge graphs (G), abstract patterns (P), and final answers ( A). Inspired by category theory, it ...

but the reality is that although these pure graph theory algorithms are decidedly influential, they cannot be applied verbatim to the reality of graph traversal between destinations in the ...

Category theory has been finding increasing applications in machine learning. This repository aims to list all of the relevant papers, grouped by fields. For an introduction to the ideas behind ...

This research semester programme is focused on understanding machine learning through the lens of mathematics and computer science, including especially statistics, combinatorics, convex analysis, ...

graph theory, randomized computation, computational learning theory, probabilistic methods and combinatorics. A major focus of the group is on the design and analysis of provably efficient algorithms ...