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
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:
- 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
- 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).