CSE.0002x is an introduction to using computing to solve problems in engineering and science including simulation of time-dependent phenomena; optimization of systems; and quantification of uncertainty.
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CSE.0002x will teach you how to use to solve problems in engineering and science including simulation of time-dependent phenomena; optimization of systems; and quantification of uncertainty. This course is primarily for learners with some prior programming experience in Python and an introductory knowledge of calculus and mechanics (typical of a first-year college course in these topics). You will spend a considerable amount of time writing programs to implement the concepts covered in the course. For example, you will write programs that will: simulate the descent of a lander in the Martian atmosphere; optimize the placement of cellular towers on the MIT campus; and quantify the likelihood of significant climate temperature rise under different scenarios.
Topics covered include:
- Advanced programming in Python 3 and NumPy
- Plotting with Matplotlib
- Initial value problems
- Discretization with explicit and implicit methods
- Solution of linear and nonlinear systems of equations
- Unconstrained optimization and gradient descent
- Probability, distributions
- Monte Carlo simulations
What you'll learn
- Python 3, NumPy, and Matplotlib
- Numerical methods to solve initial value problems
- Solution of linear and nonlinear systems of equations
- Unconstrained optimization and gradient descent
- Monte Carlo simulations
Prerequisites:
- 6.00.1x- Introduction to Computer Science and Programming Using Python Introduction to computer science or equivalent
- Some prior programming experience in Python
- Introductory knowledge of calculus and mechanics (typical of a first-year college course in these topics)
