MLSys-NYU-2022

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MLSys-NYU-2022 is a Research & Education tool that provides slides, scripts, and materials for the Machine Learning in Finance course at NYU Tandon. It offers open-source educational content for students and practitioners.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is MLSys-NYU-2022?

MLSys-NYU-2022 is an open-source repository containing slides, scripts, and materials from the Machine Learning in Finance course at NYU Tandon, 2022. Developed by Prof. Ethan Rosenthal and Jacopo Tagliabue, this resource focuses on practical applications of ML in finance, emphasizing industry-standard tools and good coding habits. It covers topics like MLOps, fraud detection, recommender systems, and evaluation metrics. The course materials are structured by week, with self-contained folders including READMEs, scripts, notebooks, and slides, each with a dedicated `requirements.txt` for reproducibility. It also introduces tools like Metaflow, Streamlit, Comet, and Flask for building and deploying ML pipelines and applications.

Best used for

Ideal for professors and teachers who need to develop or enhance a machine learning in finance curriculum, students seeking practical ML knowledge with real-world financial applications, and practitioners looking to understand industry-standard MLOps tools. Especially valuable for those emphasizing good coding habits and reproducible research.

Common actions

learn machine learning

teach machine learning

explore MLOps

study finance applications

"AI Agents"github copilotface swappingautomated workflowopen-sourcedeepfakeworkflowslow-code/no-codecollaboration

Capabilities

Key features

Machine Learning course materials
Finance use cases
Python scripts
Jupyter notebooks
MLOps best practices
Virtual environment setup
Reproducible code

Target Audience

professorteacherstudent

Integrations

metaflowstreamlitcometflask

Pricing & Plans

Open Source

Free

FAQs

What specific topics are covered in the MLSys-NYU-2022 course materials?

The course covers an introduction to ML in Finance, Python setup for scientific computing, ML best practices (dataset split, hyper-parameter tuning), classification, regression, fraud detection, MLOps best practices (experiment tracking, DAG-based pipelines, deployment), slice-based metrics, embeddings, and recommender systems.

Are the course materials suitable for beginners in machine learning?

While the course provides an introduction to ML, it focuses on practical applications and industry-standard tools, assuming some foundational technical understanding. It emphasizes good coding habits and real-world problem-solving, making it more suitable for those with basic programming knowledge.

How are the materials structured for learning and reproducibility?

The repository is structured by week, with each week having its own folder containing a README, scripts, notebooks, and slides. Each week also includes a dedicated `requirements.txt` file to ensure the scripts are reproducible, promoting good development practices.

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