<ul><li>Vectors are fundamental in representing financial data for portfolio management and risk analysis.</li><li>Matrices play a key role in portfolio analysis for calculations of covariance, correlation, risk decomposition, and eigen decomposition.</li><li>Techniques such as Singular Value Decomposition (SVD), linear regression, Positive Definite Matrices, Markov chains, Principal Component Analysis (PCA), Singular Spectrum Analysis (SSA), and Kalman filters are crucial in financial applications.</li><li>Understanding linear algebra fundamentals is essential for developing quantitative finance and machine learning models, enabling informed decision-making and robust risk management systems.</li></ul>

Fundamentals of Linear Algebra for Quantitative Finance and Machine Learning

Discover more