Course 2: Advanced Data Science
The course “Advanced Data Science” provides an in-depth discussion of machine learning/artificial intelligence models and their applications to quantitative finance.
In particular, this course covers the following topics:
- Machine learning models for point and probabilistic prediction, including supervised regression and classification, unsupervised autoencoders and graphical models
- Decision theory and estimation risk assessment via cross-validation and Monte Carlo: variance /bias tradeoffs
- Feature engineering and enhancements: feature bases, trees, neural networks, gradient boosting, lasso/ridge regularization, random forests etc
- Financial applications of machine learning models for hedging, credit default and clustering
After the course you will obtain a Statement of Completion. Furthermore, you can choose to complete an optional code-based project that will count as one of the two Practical Projects towards the ARPM Certification.
Decision theory
Machine learning foundations
Estimation assessment and enhancements
Supervised learning: regression
Supervised learning: classification
Unsupervised learning: autoencoders and graphical models
