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Content

Introduction to Machine Learning and Quantum Computing

21.04.2022 – 22.04.2022


Schedule

  • April 21st: Machine Learning, Dr. Benedikt Wilbertz
  • April 22nd: Quantum Computing, Prof. Dr. Christian Fries
  • April 23rd: Optional exercise sesssion

Venue

The workshop takes place online, on Zoom. The Zoom data will be send via e-mail after registration.

Contact

christian.fries@math.lmu.de

Flyer

Tentative Schedule

Thursday

09:00 - 10:30
11:00 - 12:30

12:30 - 14:00 lunch

14:00 - 15:30
16:00 - 17:30

Friday

09:00 - 10:30
11:00 - 12:30

12:30 - 14:00 lunch

14:00 - 15:30
16:00 - 17:30

Saturday

TBA (likely 09:00 to 12:00)

 Tentative agenda:

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 Forests
    • Gradient Boosting
    • Model Ensembling
  • Deep Learning
    • Stochastic gradient descent and optimization for neural networks
    • Neural network architectures and applications
  • Model Interpretability
    • Visualizations
    • Causal Modeling
Quantum Computing
  • Mathematical Foundations
  • Tensor Space, Linear Operators
  • Qubit, Quantum Register
  • Entanglement
  • Quantum Gates
  • Basic Algorithms
    • Grover Algorithm
    • Amplitude Estimation
    • Quantum Error Correction
  • QC versus Classical Computing
    • Programmer’s view on QC
  • Quantum Computing Frameworks
    • Circ
  • Application from Mathematical Finance
  • Hands-On Numerical Experiments

Helpful Knowledge

Basic knowledge of R or Python for Machine Learning; Basis knowledge of Python or other Languages (Java, C++, C#, C) for Quantum Computing; Basic knowledge 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.



 

Registration and Contact

To register send an email to: christian.fries@math.lmu.de

Workshop fee

The payment of a workshop fee of 300€ is required.

Note: Students form LMU/TUM, University of Verona, University of Wuppertal should visit/register for the lecture Introduction to Machine Learning and Quantum Computing. This lecture will have a final exam (and of course no fee applies).