Lehigh University’s MS in Financial Engineering makes its debut in the Risk.net Quant Guide this year. It’s among the smaller programmes represented in the guide, with 13 full-time students who will benefit from the close supervision of the programme’s nine instructors. The course is led by programme manager and professor of practice Patrick Zoro, formerly of BNP Paribas.
The programme can be completed in a single year, but most students choose to take it in two. The curriculum includes a wide array of compulsory modules, a required machine learning element and a range of electives.
The mandatory or ‘core’ classes include modules in: statistical computing; derivatives and risk management; financial optimisation; and a ‘practicum’ course, where students take part in a project with a real-world financial services firm. Practicums involve the creation of “an analysis, tool or product of potential value to the project sponsor”, according to the programme.
In the required machine learning element, students choose one module from a set of three. The available topics are: “fundamentals of machine learning”, a class focusing on Bayesian decision theory and neural nets; “introduction to machine learning”, which tackles applied machine learning techniques and programming solutions for machine learning problems; and “statistical machine learning”, which explores fields such as parametric regression, model selection and ensemble learning.
Electives are chosen from: the quantitative risk track, which contains three modules; the data science and financial analytics track, which contains five; and the financial operation track, which contains four.
Because of the impact of Covid-19, classes have been taught in a hybrid format throughout the autumn semester, says Zoro. If students want to learn on campus, they do so in classes of reduced size to comply with social distancing guidelines. Zoro adds that the small class sizes have the benefit of allowing for “much more personal interaction between the faculty and the student”.
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