The field of machine learning has revolutionized the way we approach complex problems and make intelligent decisions. In this talk, I am going to firstly give an introduction to machine learning with a journey through the key concepts, techniques, and methodologies that shape the field. Starting with a brief overview, we will explore the core integrentsof machine learning, highlighting its transformative applications across various domains. Subsequently I will focus on the mathematical frameworks that govern error analysis, including bias-variance tradeoffs, overfitting and underfitting, and model complexity. The mathematical analysis will enable us to understand and quantify the performance of machine learning algorithms. This includes a look at empirical risk minimization, generalization ability, and the role of hypothesis spaces.
