Linear Algebra
Linear Algebra is a branch of mathematics that plays a fundamental role in various fields, ranging from physics and engineering to economics and computer science. In recent decades, the advancements in technology and data science have further emphasized the importance of Linear Algebra, particularly in finance, business, and machine learning. This book is designed to bridge the understanding of the basic theories of Linear Algebra with its applications in modern contexts, where data analysis and decision optimization are increasingly essential for strategic decision-making.
In the world of finance and business, Linear Algebra plays a key role in portfolio analysis, risk management, and in modeling and predicting market trends. Understanding vectors, matrices, and linear transformations is crucial for solving various problems, such as investment optimization, stock price forecasting, and regression analysis.
On the other hand, machine learning heavily relies on Linear Algebra to build efficient learning algorithms, especially in processing large-scale data. Concepts such as matrix decomposition, eigenvalues, and quadratic optimization methods enable the development of robust machine learning models that can be applied in various fields, from pattern recognition to big data management.