Foundations of Generative AI and Applied Machine Learning

"Applied machine learning is reshaping the way we interact with technology, turning vast amounts of data into useful insights and smarter decisions. One of its most visible and powerful applications is in recommender systems, which helps people discover what to watch, read, buy, or explore next. By analyzing patterns in user behavior and preferences, recommender systems deliver personalized suggestions that make our digital experiences more efficient, enjoyable, and relevant. From streaming platforms and online shopping to educational tools and research discovery, applied machine learning and recommender systems are changing the way we access information, make choices, and connect with the world around us. In a broader context of machine learning, Artificial Intelligence (AI) has rapidly become integrated into human life over the past few years. This integration, however, has not occurred uniformly worldwide; it has been most pronounced in technologically advanced regions such as the European Union, the United States and East Asia and influences almost all domains. The speed and scale of this transformation offer extraordinary opportunities, but also carry risks of disruption and unintended consequences. Large language models are moving from being static pre-trained models to becoming adaptive, self-improving systems that can dynamically retrain on domain-specific knowledge or user feedback in near real time. Within this shift, deep learning and generative AI models are redefining traditional methods of inquiry and problem solving. Together, these technologies have the potential to accelerate knowledge discovery while reshaping human life on a societal scale, influencing institutions, economies, and cultural practices. The promise is clear: AI can reduce the time spent on routine tasks, and it also opens new possibilities for connection and collaboration in diverse fields. In this way, AI acts as catalysts and amplifiers of human progress. However, these gains come with challenges. Rapid adoption of such technologies, particularly generative AI, raises urgent concerns about bias, misuse, ethics, and trust. The textbook is intended for students of generative AI and applied machine learning courses, as well as the general professional public dealing with or interested in data science and AI. On the one hand, it explores the practical applications of generative AI and applied machine learning, with recommender systems highlighted as the most well-known use case, providing hands-on techniques and real-world case studies. On the other hand, it provides a brief overview of machine learning governance, including MLOps for scalable development and deployment, and privacy-preserving machine learning techniques to ensure responsible AI practices.