The Jupyter Guide to Linear Algebra covers many of the core topics that would appear in an introductory course on linear algebra, together with several applications. The guide also provides a brief introduction to Python, with focus on the portions that are relevant to linear algebra computations. The Jupyter Guide to Linear Algebra should prove useful to students in a traditional first-year university course on linear algebra.
Features:
- Development of a module to be used along side of the Jupyter Guide to Linear Algebra, or independently.
- Exercises aimed at exploring linear algebra concepts, as well as exercises to practice writing Python code.
- Instruction on the basic use of NumPy, SciPy, and Matplotlib.
- Introduction to Hill ciphers, Markov processes, least squares solutions, and other applications of linear algebra.