Mathematical modeling is a vehicle that allows for explanation and prediction of natural phenomena. In this chapter we present guidelines and best practices for developing and implementing mathematical models, using cancer growth, chemotherapy, and immunotherapy modeling as examples....
My main research interest lies in mathematical and philosophical logic, particularly set theory, with a focus on the mathematics and philosophy of the infinite. I have worked particularly with forcing and large cardinals, those strong axioms of infinity, and have investigated the interaction of these...
1. Best books on artificial intelligence for business leaders Artificial intelligence is here to stay – and it’s going to be pretty transformative for any department that adopts it. For business leaders, that incoming change will require understanding AI’s real-world use cases, its pitfalls, ...
An Introduction to Mathematical Modeling (Dover Books on Computer Science) Edward A. Bender 4.5 颗星,最多 5 颗星 62 Kindle电子书 US$9.99 #19 Fundamentals of Discrete Math for Computer Science: A Problem-Solving Primer (Undergraduate Topics in Computer Science) Tom Jenkyns 3.3 颗星,最多 5 ...
Mathematical Methods for Quantitative Finance Supply Chain Analytics Supply Chain Fundamentals Sustainable Building Design Sustainable Energy Understanding the World Through Data World Music: Global Rhythms It's important to note that these free courses do not come with a certificate of completion, but tha...
To generate their tone modeling amps use computer programmed chips with mathematical models that replicate the effect that analog components have on electric guitar signals. Researchers studied using Sine waves and made observations of howchanging settings in vacuum tube amps incrementally changed the init...
Learn Data Science in 2023 with the best Data Science courses, best Data Science tutorials & best Data Science books.
This course will introduce you to statistical analyses, mathematical modelling, probability, and optimization techniques, Supervised and unsupervised learning models, advanced machine learning applications, deep learning concepts and applications, etc.
It is typically assumed that large networks of neurons exhibit a large repertoire of nonlinear behaviours. Here we challenge this assumption by leveraging mathematical models derived from measurements of local field potentials via intracranial electroenc
This open course from MIT provides an introduction to computational problems and mathematical modeling. It goes over the most prevalent algorithms, algorithmic paradigms, and data structures for solving these issues. The focus of the course is on the interaction between algorithms and programming, as ...