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Quant finance lecture notes

97 lecture notes selected for learning quantitative finance. Highlighted picks come first. Showing 51–97; page 2 of 2.

  1. Mathematical Finance 345 / Statistics 390: Homework 9 (UChicago, 2001)

    Steven P. Lalley

    asset pricing · derivatives · mathematical finance · option pricing · probability · stochastic calculus

  2. Mathematical Foundations of Machine Learning

    Robert Nowak · 2022

    bayesian inference · kernel methods · neural networks · optimization · probability · statistical learning theory

  3. Mathematics of Computational Finance

    Andrzej Palczewski, University of Warsaw · 2022

    american options · computational finance · derivative pricing · finite difference methods · finite element methods · monte carlo methods

  4. Maths You Need for Graduate Studies in Quantitative Finance

    Alexandre Landi

    linear algebra

  5. Matrix Calculus (for Machine Learning and Beyond)

    Alan Edelman, Paige Bright, Steven G. Johnson · 2023

    Course Notes · Linear Algebra · MIT · Machine Learning · Matrix Calculus

  6. Max Dama on Automated Trading

    Max Dama · 2011

    Algorithms · Automated Trading · Quantitative Trading

  7. Mean-Variance Optimization and the CAPM (Columbia IEOR E4706)

    Martin Haugh

    asset pricing · capm · mathematical finance · optimization · portfolio management · portfolio optimization

  8. Multiple Hypothesis Testing

    Chuan Shi

    factor investing · hypothesis testing · probability · statistical inference · statistics

  9. Notes on Quantitative Finance

    Jimmy Qin · 2019

    derivatives · forwards · futures · interest rates · no-arbitrage · options

  10. Optimization Methods in Finance

    Gerard Cornuejols, Reha Tütüncü · 2006

    CMU · Carnegie Mellon · Finance · Mathematical Finance · Optimization

  11. Other Topics in Quantitative Finance (Columbia IEOR E4706)

    Martin Haugh

    corporate finance · financial engineering

  12. Poisson Processes (Chapter 9)

    David Pollard

    probability · statistics · stochastic processes

  13. Portfolio Sort Analysis

    Chuan Shi

    asset pricing · factor investing

  14. Princeton Measure Theory

    Princeton University, Shapiro · 2025

    Lecture Notes · Measure Theory · Princeton · Real Analysis

  15. Probability & Markets

    Jane Street

    adverse selection · conditional probability · confidence intervals · counting · expected value · financial markets

  16. Quant Trading Guide

    Callum McDougall · 2020

    algorithmic trading · financial markets · internships · market making · math interviews · probability puzzles

  17. Regression-Based Test

    Chuan Shi

    asset pricing · econometrics · factor investing · hypothesis testing · regression · statistical inference

  18. Statistics Cheatsheet With Python Codes and Visualization

    soorabcode · 2025

    anova · bayes theorem · binomial distribution · confidence intervals · correlation · data science

  19. Stochastic Calculus - An Introduction with Applications

    Gregory F. Lawler · 2015

    Brownian motion · Itô calculus · applications in finance · continuous-time models · martingales · probability theory

  20. Stochastic Calculus: Class 11 — Ito (NYU, 2019)

    Jonathan Goodman

    brownian motion · calculus · stochastic calculus · stochastic differential equations · stochastic processes

  21. Stochastic Calculus: Class 12 — Ito, theory (NYU, 2019)

    Jonathan Goodman

    brownian motion · calculus · probability · stochastic calculus · stochastic processes

  22. Stochastic Calculus: Class 13 — change of measure (NYU, 2019)

    Jonathan Goodman

    brownian motion · calculus · probability · stochastic calculus · stochastic processes

  23. Stochastic Calculus: Class 2 — Brownian motion and diffusion equations (NYU, 2019)

    Jonathan Goodman

    brownian motion · calculus · partial differential equations · probability · stochastic calculus · stochastic processes

  24. Stochastic Calculus: Class 3 — Backward equations (NYU, 2019)

    Jonathan Goodman

    brownian motion · calculus · partial differential equations · probability · stochastic calculus · stochastic processes

  25. Stochastic Calculus: Class 4 — Ito integral for Brownian motion (NYU, 2019)

    Jonathan Goodman

    brownian motion · calculus · stochastic calculus · stochastic processes

  26. Stochastic Calculus: Class 5 — multi-component diffusions (NYU, 2019)

    Jonathan Goodman

    calculus · probability · stochastic calculus · stochastic differential equations · stochastic processes

  27. Stochastic Calculus: Class 6 — linear problems, more (NYU, 2019)

    Jonathan Goodman

    calculus · probability · stochastic calculus · stochastic differential equations · stochastic processes

  28. Stochastic Calculus: Class 8 — More differential equations (NYU, 2019)

    Jonathan Goodman

    calculus · partial differential equations · probability · stochastic calculus · stochastic differential equations · stochastic processes

  29. Stochastic Calculus: Class 9 — More differential equations, continued (NYU, 2019)

    Jonathan Goodman

    calculus · partial differential equations · probability · stochastic calculus · stochastic processes

  30. Stochastic Calculus: Final Exam Practice (NYU, 2018)

    Jonathan Goodman

    brownian motion · calculus · martingales · probability · stochastic calculus · stochastic differential equations

  31. Stochastic Calculus: Lesson 1 — Brownian motion (NYU, 2018)

    Jonathan Goodman

    brownian motion · calculus · probability · stochastic calculus · stochastic processes

  32. Stochastic Calculus: Lesson 2 — Diffusion processes (NYU, 2018)

    Jonathan Goodman

    calculus · stochastic calculus · stochastic differential equations · stochastic processes

  33. Stochastic Calculus: Lesson 3 — Ito integral (NYU, 2018)

    Jonathan Goodman

    probability · stochastic calculus · stochastic differential equations · stochastic processes

  34. Stochastic Calculus: Lesson 4 — Ito’s lemma (NYU, 2018)

    Jonathan Goodman

    calculus · stochastic calculus · stochastic processes

  35. Stochastic Calculus: Lesson 5 — SDE and PDE (NYU, 2018)

    Jonathan Goodman

    calculus · partial differential equations · probability · stochastic calculus · stochastic differential equations · stochastic processes

  36. Stochastic Calculus: Lesson 6 — Simulating, change of measure (NYU, 2018)

    Jonathan Goodman

    calculus · monte carlo simulation · numerical methods · probability · stochastic calculus · stochastic differential equations

  37. Stochastic Calculus: Lesson 8 — Multi-component diffusion (NYU, 2018)

    Jonathan Goodman

    calculus · partial differential equations · probability · stochastic calculus · stochastic processes

  38. Stochastic Differential Equations (MIT 18.S096, Lecture 21)

    Choongbum Lee, MIT OpenCourseWare

    numerical methods · probability · stochastic calculus · stochastic differential equations · stochastic processes

  39. Stochastic Processes I (MIT 18.S096, Lecture 5)

    Choongbum Lee, MIT OpenCourseWare

    probability · stochastic processes

  40. Stochastic Processes II (MIT 18.S096, Lecture 17)

    Choongbum Lee, MIT OpenCourseWare

    brownian motion · probability · stochastic processes

  41. Term Structure Lattice Models (Columbia IEOR E4706)

    Martin Haugh

    derivatives · financial engineering · fixed income · interest rates · martingales · mathematical finance

  42. The Black-Scholes Model (Columbia IEOR E4706)

    Martin Haugh

    derivatives · financial engineering · mathematical finance · option pricing · volatility surface

  43. The Fundamental Theorem Of Arbitrage Pricing (UChicago, 2001)

    Steven P. Lalley

    asset pricing · derivatives · mathematical finance · option pricing

  44. The Peter Lynch Playbook

    equity analysis · fundamental analysis · growth investing · long term investing · portfolio allocation · risk reward

  45. The Principles of Deep Learning Theory

    Boris Hanin, Daniel A. Roberts, Sho Yaida · 2021

    Deep Learning · Effective Theory · Facebook · MIT · Machine Learning · Neural Networks

  46. Theory of Deep Learning

    SANJEEV ARORA · 2020

    Deep Learning · Machine Learning · Mathematics · Neural Networks · Theory of Deep Learning

  47. Undergraduate Fundamentals of Machine Learning

    Finale Doshi-Velez, Harvard University, William J. Deuschle · 2018

    Fundamentals · Harvard · Machine Learning · Supervised & Unsupervised Learning · Undergraduate Thesis