Use modern machine learning tools and python libraries. Explain how to deal with linearly-inseparable data. Compare logistic regression’s strengths and weaknesses. Explain what decision tree is & how ...

The teaching and learning of linear algebra have evolved significantly over recent decades, underpinned by diverse approaches ranging from theoretical expositions to dynamic, model-based environments.

Professor Li Hang's new book is positioned as a university textbook and a reference for professionals, suitable for readers with a certain background in calculus, linear algebra, and computer science.

The time-tested technique for predicting numbers, and the role of domain knowledge in machine learning.

This is a 10-week bridge course that prepares students for the fundamentals of linear algebra as they are used in Applied Statistics courses.