Logo ARPM
Logo ARPM
start here
  • Quant Bootcamp
    • Quant Bootcamp is a 4+2-day intensive program in Machine Learning for quantitative finance, combining expert instruction with professional networking.

    • Reviews
      Alumni's voices about the Quant Bootcamp

      FAQs

      Enroll

      Join Info Session

    • Overview
    • Reviews
    • FAQs
    • Enroll
    • Join Info Session
  • Quant Marathon
    • Quant Marathon is a 5+5-month long, multi-course program offering a structured learning path in Machine Learning and quantitative finance, with live classes and a certification track.

    • Courses
      All-encompassing, mutually exclusive, in-depth

      Reviews
      Alumni's voices about the Quant Marathon

      FAQs

      Enroll

      Join Info Session

    • Overview
    • Courses
    • Reviews
    • FAQs
    • Enroll
    • Join Info Session
  • Lab
    • The Lab is ARPM’s integrated e-textbook, unifying theory, code, case studies, and exercises in a single mathematical framework.

    • Machine Learning
      Mathematical Statistics for Finance, Linear Mean-Covariance Statistics, Probabilistic Machine Learning, Time Series and Sequential Decisions

      Quant Finance
      Financial Engineering, Portfolio and Enterprise Risk Management, Portfolio Construction and Trading

      Primers
      Mathematics, Finance and Python

      Enroll

    • Overview
    • Machine Learning
    • Quant Finance
    • Primers
    • Enroll
  • start here contact us login
contact us
login

Training in Machine Learning for Finance

ARPM builds advanced competence to work in modern Financial Engineering, Risk Management and Quantitative Investment

The next Quant Marathon starts on February 16

ARPM lab

Beyond-master online program: 5+5-month, multi-course, in-depth

Structured curriculum with live classes, support, and certification path

learn more

The next Quant Bootcamp starts on July 13 at New York University

ARPM bootcamp

World-renowned guest speakers

Hundreds of quants from top financial firms

learn more

Lab: Animations, Code, and Mathematics for Deeper Learning

ARPM lab

Animations, to boost intuition

Code, to learn by doing

Mathematics, for deeper understanding

learn more

Previous Next

Why ARPM?

  1. Innovation in AI is chaotic and relentless: to know which technique to apply, and how, and remain competitive you need to understand the core principles before new advanced techniques.

    ARPM delivers to you

    • The “Mean-covariance/Probabilistic symmetry” – a framework for organizing all of Machine Learning from the simplest principles to the most advanced innovations
    • The “10-Step Checklist” – a framework for organizing the entire fields of Financial Engineering, Risk Management and Quantitative Investment
  2. Your time is scarce: you need to delve from first principles into advanced, disparate topics as quickly as possible.

    Unlike most programs that are taught on a collection of scattered references, all ARPM learning is delivered through the Lab

E-Textbook

Theory icon
Theory Mathematical formalism, for faster learning
Case studies icon
Case studies Applications, for deeper learning

Complementary Learning Resources

Code icon
Code Learn by doing, no installation
Animations icon
Animations Intuition in motion
Slides icon
Slides Summary of all key items
Proofs icon
Proofs Don't believe, verify
Exercises icon
Exercises Code based and analytical

The Lab is an extensive e-textbook, with code and additional resources, written in succinct, unified, mathematical language, constantly curated for consistency, and powered by an AI chatbot for easy access. No gaps, no overlaps

Upon graduation, you will retain access to the Lab for life.

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
Linear Mean-Covariance Learning Linear Mean-Covariance Learning
Analytically tractable foundations of advanced 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
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, three complementary ways to learn

Quant Bootcamp

4+2-day, full-immersion, overview course at New York University/streaming

Starts July 13
Learn more
Quant Bootcamp presentation

Quant Marathon

Multi-course, 5+5-month, in-depth, beyond-master program, with certification, remotely

Starts February 16
Learn more
Quant Marathon presentation

Lab

Interactive e-learning platform with theory, code, animations, and an AI personal trainer

Always available
Learn more
Lab presentation

Our Stats

5,000+ alumni

Quant Bootcamp-ers and Quant Marathon-ers, 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

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.

"I liked the number of topics that were covered and the depth along with the documentation support for each of these topics..." see more

Tejus Setlur

Quantitative Analyst

"The structure of the course is well thought out, with a mix of theory sessions and then applicable case studies where we could apply the theory in practice..." see more

Simon Kanani

Quantitative Data Analyst / Developer

"The Bootcamp is great challenge (learning a lot of new concepts) and quizzes designed to test understanding..." see more

Emlyn Flint

Derivatives & Quant Researcher

"I liked very much the scope and the clarity of complex topics. Also the alignment of notation and definitions of so many topics is impressive..." see more

Stephan Krushev

Data Scientist

"I liked the breadth of the arguments that are presented and also how in-depth they are discussed..." see more

Alessandro Pogliani

Quantitative Trader

"Practice sessions were nice and guests, and of course the lab is very well done!..." see more

Federico Ron

Multi-Assets Quantitative Analyst

"Hi, I took the ARPM Quant Marathon in 2019, lasting one year and I can share with you my personal experience. First of all, I can confirm that it is really worth it...." see more

Daniele Pennesi

Portfolio Manager

Previous Next
 
arpm small logo
About us Start here
Clients and partners Corporate program Academia program
Contact us Book
Contact us   Linkedin
Terms ⚪ Privacy policy ⚪ Refund policy ⚪ Cookies policy ⚪ Copyright ⚪ IT requirements
© 2026 ARPM, All rights reserved.