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Content

Machine Learning and Algorithmic Differentiation

20.02.2020 – 22.02.2020


Schedule

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

Venue

The workshop takes place at

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

A detailed location plan can be found here.

Contact

christian.fries@math.lmu.de

Flyer

Tentative Schedule

Thursday

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

Friday

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

Saturday

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 http://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 table:

Rate Type of Participant
 950€  Practitioners
 350€  Academics

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.