Machine Learning and Computational Statistics

(3 Credits)

This full-semester course integrates key elements of econometrics, data science, and machine learning in a financial context. Students will learn to model financial data using statistical techniques and computational methods while gaining hands-on experience with Python-based tools. The course emphasizes: ● Financial Econometrics & Statistical Inference: Understanding linear regression frameworks, hypothesis testing, and model selection. ● Supervised Learning: Implementing regression and classification models, optimizing performance through cross-validation and regularization. ● Unsupervised Learning: Applying dimensionality reduction techniques such as PCA and SVD. ● Machine Learning in Finance: Exploring algorithmic trading, risk modeling, and portfolio optimization using advanced methods like boosting, bagging, and deep learning. ● Data Manipulation & Web Scraping: Handling real-world financial data, including structured and alternative datasets, using Python. Hands-on assignments will reinforce theoretical concepts, ensuring students gain practical experience in implementing machine learning techniques for financial applications.