Statistical Foundations of Machine Learning

Catalog description

This course will introduce students to the fundamental concepts of Machine Learning. These include (i) the type of data that can be used, the data structure needed to build predictive models, feature engineering (ii) the principles of training and testing predictive models and the idea of cross-validation (iii) bias (iv) interpretability of models (v) evaluation metrics of models (vi) an introduction to unsupervised learning As data collected over time is key to many Fintech applications, we will also discuss some of the key ideas of time series analysis and the challenges inherent in that.