Explainable Artificial Intelligence (XAI) is a rapidly growing subfield of artificial intelligence (AI) that aims to make AI models and systems more transparent and understandable to humans. The goal of XAI is to bridge the gap between the black box nature of many AI models and human interpretability, to enable users to understand why and

Stochastic processes are an essential mathematical framework for understanding and modeling random phenomena. They have numerous applications in fields such as finance, engineering, and biology. One of the most well-known and influential works in this field is Stochastic Processes: Theory for Applications by Robert G. Gallager. Gallager is a renowned American electrical engineer and information

Introduction Deep Learning has revolutionized the field of artificial intelligence and has become one of the most sought-after skills for developers and data scientists. With the increasing availability of data and computing power, it has become easier for individuals to learn and apply deep learning techniques in their projects. However, the complexity and unfamiliarity of

Introduction to Machine Learning: Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar Machine learning has become an integral part of many industries and applications in recent years. It is a subset of artificial intelligence that focuses on developing algorithms and techniques that enable computers to learn from data without being explicitly programmed. One of the most

Graph representation learning is a rapidly growing field in machine learning, with a large body of research and advancements being led by William L. Hamilton. Hamilton is a Canadian computer scientist, currently serving as an assistant professor at the School of Engineering and Applied Sciences at Harvard University. He received his PhD from Stanford University