Course 2: Advanced Data Science
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 ensemble learning, regularization and cross-validation
- 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.
Machine learning foundations
Decision theory
Estimation theory and risk mitigation
Supervised learning - regression
Supervised learning - classification
Unsupervised learning
