Please report errors/typos and issues at https://github.com/jkitchin/f19-06623/issues.

This is a set of course notes. Each link below corresponds to about a 90 minute lecture, and they are in the order the lectures were given.

Table of contents:

1. A brief introduction
2. About Jupyter
3. Numerical integration
4. First order differential equations
5. Families of first order differential equations
6. N^th order ODEs
7. Introduction to nonlinear algebra
8. Polynomials and systems of nonlinear algebraic equations
9. Boundary value problems
10. Finding minima and maxima in functions
11. Introduction to regression
12. Nonlinear regression
13. Constrained optimization
14. Introduction to linear algebra
15. Linear algebra
16. Interpolation and eigenvalues
17. Linear regression and regularization
18. Introduction to derivatives
19. Applications of autograd
20. Introduction to machine learning
21. ML and extrapolation
22. Gaussian process regression

That concludes these lectures!