Introduction to Machine Learning and Algorithmic Differentiation
Lead by Dr. Benedikt Wilbertz and Prof. Dr. Christian Fries
Aufgrund eines einschneidenden privaten Ereignisses kann die Vorlesung "Introduction to Machine Learning and Algorithmic Differentiation" leider nicht stattfinden. Wir bemühen uns darum die Veranstaltung zu einem späteren Zeitpunkt erneut anbieten zu können.
Wir bedauern etwaig entstandene Unannehmlichkeiten.
Due to a drastic private event the lecture "Introduction to Machine Learning and Algorithmic Differentiation" cannot take place. We will do our best to offer the event again at a later date.
We regret any inconvenience.
- 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
- Causal Modeling
- Introduction to Algorithmic Differentiation
- Algorithmic Differentiation (AD)
- Adjoint AD (AAD)
- Enabling Software Design Patterns
- 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
Target Participants: Master students of Mathematics or Business Mathematics.
Pre-requisites: Basic knowledge of R (for Machine Learning) and of Java / Object Oriented Programming (for AAD). Basics in options pricing theory (for Applications from Finance).
Applicable credits: Students will receive 3 ECTS Points upon successful participation that may be attributed to the module WP20, WP22 or WP43 for students enrolled in the LMU Master Mathematics programme.
The written exam is open-book, that is, all notes, books, solutions of exercises etc. may be used. Personal electronic devices of any kind are not allowed. To participate, please bring to the exam your ID card or passport and your student card. Please be on time.