Course description
A quick introduction to applied stochastic calculus. Properties of Brownian motion, Ito calculus, SDE, and simulation. Value functions and the relation to PDE. A bit about stochastic control. Girsanov theorem.
Prerequisites:
Good linear algebra, multi-variate calculus, A good upper level undergraduate probability class, Python programming
Assignments, exams, grading:
The final grade will be based on weekly homework assignments (worth about 50% of the grade), and an in-class written final exam (worth about 50%).
Communication:
Please use the Brightspace site for content and homework communications. This way everyone sees and benefits from questions and answers, and there can be class discussion. related comm. Email the instructor for issues that do not involve others such as scheduling appointments, homework extensions, advice, etc.
Academic integrity:
Students are encouraged to explore and collaborate widely to understand the material. This includes looking at print and online sources and interacting with experts and each other. Students may receive some help with assignments, but each student must create (write up, code, run) solutions individually. Students may not share ("borrow" or lend) assignment solutions -- all writing must be done individually. Students may not plagairize solutions from other sources such as books or web sites. Assignments may not be crowd-sourced on any web platform. Do not post homework exercises. Violation of these policies may result in grade lowering or more serious penalties, depending on severity.