Machine Learning and Information Science – Mathematical Association of America

Featured Resources

Filling in Missing Data Using Intuitive Logic: Deep-Sea Coral Edition

By Kelly Donovan Initial interest Throughout my life, I’ve always been interested in the environment, climate, weather, and the physical aspects of the Earth. I knew I wanted to pursue research that would let me dip my toes into a topic that was underrepresented, so when my research mentor provided me with a path that...

What does it mean to ask, “Who is math?”

By Aji Kang Who is math to you? To me, math is a funny friend who reminds me that I have so much to learn, who shows me that I gain a lot from letting go of ideas I hold on to, and who patiently waits for me to mature enough to see past the...

Linear Algebra, Data Science, and Machine Learning

This introduction to modern methods of machine learning and data analysis is a mathematically rigorous and challenging but highly readable account with a remarkably wide scope. It is largely self-contained and accessible at the upper undergraduate level. The authors emphasize the importance of understanding the how and why their algorithms work, what they do, and...

Computability and Complexity

This book provides an introduction to the theory of computation. It is aimed primarily at undergraduate computer science students who are comfortable with mathematical notation, definitions and proofs, and especially with reading a detailed mathematical presentation. The author also intends his book to be useful to others in disciplines that depend in some way on...

Inverse Problems and Data Assimilation

Inverse problems and the associated questions of data assimilation are of considerable interest in our data-intensive world. This book offers a concise introduction to these subjects from a mathematical perspective. The authors suggest it is most appropriate for advanced undergraduates and graduate students in mathematics as well as researchers in the sciences and engineering interested...