20 Feb
22 Feb

quantLab - Machine Learning and Algorithmic Differentiation


20 February 2020 - 22 February 2020



  • February 20th: Algorithmic differentiation, Prof. Dr. Christian Fries
  • February 21st: Machine Learning, Dr. Benedikt Wilbertz
  • February 22nd: Optional exercise session


quantLab - Room B 121
LMU Institute of Mathematics
Theresienstr. 39
80333 Munich

A detailed location plan can be found here.



Flyer (PDF, 317 KB)

Tentative Schedule


  • 09:00 - 10:30
  • 11:00 - 12:30
  • 12:30 - 14:00 lunch (jointly, used for discussion)
  • 14:00 - 15:30
  • 16:00 - 17:30


  • 09:00 - 10:30
  • 11:00 - 12:30
  • 12:30 - 14:00 lunch (jointly, used for discussion)
  • 14:00 - 15:30
  • 16:00 - 17:30


  • TBA (likely 09:00 to 12:00)

Tentative agenda

Algorithmic Differentiation

  • Introduction to Algorithmic Differentiation
    • Algorithmic Differentiation (AD)
    • Adjoint AD (AAD)
  • Enabling Software Design Patterns
    • Interfaces
    • Dependency Injection
  • Stochastic Algorithmic Differentiation: AAD for Monte-Carlo Simulations
    • AAD of Conditional Expectations
    • AAD of Indicator Functions
  • Application from Finance
    • Hedge Simulation
    • Margin Valuation Adjustment

Machine Learning

  • Introduction to Machine Learning
    • Concepts of supervised learning
    • Bias-Variance trade-off and model performance
    • Feature engineering
  • Linear and non-linear regression models
    • Linear models
    • Support vector machines
  • Classification models
    • Decision Trees
    • Random Forest
    • Gradient Boosting
    • Model Ensembling
  • Deep Learning
    • Stochastic gradient descent and optimization for neural networks
    • Neural network architectures and applications
  • Model Interpretability
    • Visualizations
    • Causal Modeling

Helpful Knowledge

Basic knowledge of R (for Machine Learing) and of Java / OOP (for AAD)
Basics in options pricing theory (for Applications from Finance)


Dr. Benedikt Wilbertz

Benedikt Wilbertz is currently Head of Data Science and Machine Learning at Talkwalker, a leading provider of social media analytics solutions. There he is mainly working on deep neural networks and supervised machine learning. He had been prize winner in a Kaggle competition. Beneath that he is lecturer at Sorbonne Universities Paris and holds a PhD in Probability Theory.

Prof. Dr. Christian Fries

Christian Fries is head of model development at DZ Bank’s risk control and Professor for Applied Mathematical Finance at Department of Mathematics, LMU Munich.

His current research interests are hybrid interest rate models, Monte Carlo methods, and valuation under funding and counterparty risk. His papers and lecture notes may be downloaded from www.christian-fries.de/finmath

He is the author of “Mathematical Finance: Theory, Modeling, Implementation”, Wiley, 2007 and runs www.finmath.net.


The payment of a workshop fee is required, according to the following rates:

  • Practitioners - 950€
  • Academics - 350€

Registration and Contact

The workshop will take place in a computer equipped room with limited places. To register send an email to: christian.fries@math.lmu.de

Note: Students form LMU/TU should visit/register for the lecture Introduction to Machine Learning and Algorithmic Differentiation. This lecture will have a final exam.