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Training in Machine Learning for Quantitative Finance

ARPM builds advanced statistical competence to work in modern financial engineering, risk management and quantitative investment

What you will learn

A complete, beyond master’s level curriculum that delivers unified understanding of Machine Learning and its applications to Financial Engineering, Risk Management and Quantitative Investment. No gaps, no overlaps.

Machine Learning

Quantitative Finance

Mathematical Statistics for Finance
Mathematical Statistics for Finance Linear/probabilistic statistics and decision theory for estimation/sequential decisions
Mean-Covariance Learning
Mean-Covariance Learning Analytically tractable foundations of AI
Financial Engineering
Financial Engineering Present value and future random payoff of individual instruments
Probabilistic Machine Learning
Probabilistic Machine Learning Static, causal, advanced AI modeling
Portfolio and Enterprise Risk Management
Portfolio and Enterprise Risk Management Risk measures, performance attribution, (stochastic) stress-testing
Time Series and Reinforcement Learning
Time Series and Reinforcement Learning Dynamic, causal, advanced AI modeling
Portfolio Construction and Trading
Portfolio Construction and Trading Static and dynamic strategies with optimal trade execution

Primers

Mathematics Primer
Mathematics Primer Functional analysis, optimization theory, probability
Finance Primer
Finance Primer Asset classes fundamentals, performance definitions
Python Primer
Python Primer Syntax, data types, structures, functions

How you will Learn

One advanced curriculum, delivered through three complementary learning formats.

Why ARPM?

Only ARPM has the Lab, a 4,000-page e-textbook on machine learning and its applications across all of quantitative finance, with code, animations, and an AI tutor, learn more about the Lab .

E-Textbook

Theory Theory Case studies Case studies

Complementary Learning Resources

AI tutor AI tutor Code Code Animations Animations Proofs Proofs Exercises Exercises
  1. No gaps, no overlaps. Most programs are taught on a collection of scattered references, with conflicting notation, overlapping topics and gaps. The Lab is written in succinct, unified, mathematical language and constantly updated for inclusiveness and consistency.
  2. Principles first. Innovation in AI is chaotic and relentless: to remain competitive you need to understand the core principles behind any new techniques. The Lab is structured around:
    • The “Machine Learning Ecosystem” – a framework for organizing all of Machine Learning from the simplest principles to the most advanced innovations, learn more about Machine Learning .
    • The “10-Step Checklist of Finance” – a framework for organizing the entire fields of Financial Engineering, Risk Management and Quantitative Investment, learn more about Quantitative Finance .

ARPM by the numbers

5,000+

Alumni

Quant Bootcamp-ers and Certification holders, since 2009

100,000+

Lab code lines

All case studies and examples implemented on Jupyter Lab, no installation required

3,500+

Lab pages

Overarching notation across Machine Learning and Quantitative Finance

Testimonials

Leif Andersen

Global Co-Head of Quantitative Analytics, Bank of America

Boris Deychman

Head of Model Risk Management, DTCC

Mike Sternberg

Global Head Of Aladdin Financial Engineering, BlackRock

These professionals chose ARPM for team upskilling. While company policy restricts official endorsements, they are happy to provide personal references upon request. All views expressed are their own and do not necessarily reflect those of their employers.