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Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022
| Date | Stars |
|---|---|
| 2026-07-31 | 558 |
| 2026-08-01 | 558 |
| 2026-08-06 | 558 |
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# MLSys-NYU-2022 Slides, scripts and materials for the _Machine Learning in Finance_ course at NYU Tandon, 2022. ## Overview DISCLAIMER: A significant part of our course is class participation (this is why, in the end, we have universities and not *just* books and repos!), and no amount of scripts can provide the same level of educational content, or a comparable experience. Please note that this course changes substantially every year, so the best way to keep up to date with us is... by enrolling in the [Master](https://engineering.nyu.edu/academics/programs/financial-engineering-ms)! TL;DR: This repository contains some of the teaching materials by Prof. [Ethan Rosenthal](https://www.ethanrosenthal.com/) and [myself](https://jacopotagliabue.it/) for the 2022 course in ML at the NYU Tandon School of Engineering. The course is presented as an introduction to Machine Learning with Finance use cases and industry-standard tools. We open source slides, code snippets and assignments after the class is completed, hoping to benefit the broader community of Machine Learning students and practitioners; I had a calculus book that said, "What one fool can do, another can.", and I wish more and more fools could become proficient at building reliable, trust-worthy, well-crafted ML systems. We feel there are now enough books and YouTube videos for people interested purely in the theory of ML; moreover, practitioners produce a much bigger marginal value when bringing into the class their day-to-day experience, which, for the time being, cannot be as easily found on YouTube. Therefore, the course we run is very practical and focuses on the intuitive understanding of ML problems and their solutions _through real-world tools_: we emphasize the importance of good coding habits, and the use of industry standard methodology, over complex modelling and formulas (alas, we do indeed sometimes need to talk about math). The whole course runs in 14 weeks, but we cover arguments that would keep you busy for a lifetime: every lecture, every slide, every code snippet are the result of many explicit and implicit trade-offs - what should we cover, what should we not? While no material can substitute for real-world interactions and our great sense of humour, we leave for the open source community to judge how useful the trade-offs we picked actually are. ## At a glance ### Main themes The course is structured around 14 weeks: 13 weeks of lectures, and 1 final demo day for students (organized in teams) to present an end-to-end machine learning project that showcases what they learned in the course. Main topics, roughly in order of appearance: * Introduction to ML in Finance: use cases, tools, the rise of MLOps. * Python setup for scientific computing: notebooks, environments, dependencies. * ML best practices: dataset split, hyper-parameter tuning. * Modelling: classification, regression. * Use case deep dive I: fraud detection. * MLOps best practices: experiment tracking, DAG-based pipelines, deployment. * Rounded evaluation: slice-based metrics, behavioral testing. * Introduction to embeddings: skip-gram, similarity in a latent space. * Use case deep dive II: recommender systems. ### Repo structure This year's repository is structured by week: each week has its folder, with a self-contained `README`, scripts / notebooks and slides. The choice results in some redundancy (especially in the second part, where the same training loop is used several times), but provides more clarity for students pacing themselves through the course, and highlights the highly modular nature of the syllabus. As part of the course, we emphasize the importance of virtual environments and submitting properly structured projects (notebooks are great, but we leave them for experimentation!). Each week contains a devoted `requirements.txt` file to make sure the scripts are reproducible. ### Changelog Compared to [2021 edition](https://github.com/jacopotagliabue/FREE_777
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matched fp:8d8d0626c31dbb12, topic:mlops