Introduction to Scientific Computing with Machine Learning Applications

Numerical computation for engineering and machine learning applications: error analysis, floating-point arithmetic, numerical solution of linear and nonlinear equations, optimization, gradient descent, polynomial interpolation, numerical differentiation and integration, supervised learning, numerical solution of ordinary differential equations, numerical stability, unsupervised learning, sampling (Monte Carlo algorithms). Implementation of numerical methods in programming assignments (Python or Matlab).