Personalized machine learning (PML) is a sub-field of machine learning that aims to create models and predictions based on the unique characteristics and behaviors of individual entities. While PML is commonly used in applications such as recommender systems, which recommend items to users based on their personal interests, its principles can be applied to a wide range of other fields, including education, medicine, and chemical engineering. In this course, we will explore the latest PML methods from theoretical, algorithmic, and practical perspectives. Specifically, we will focus on cutting-edge models that are of interest to both the research and commercial communities. This course is designed for students seeking an advanced understanding of personalized machine learning methods and a practical introduction to applied and/or fundamental research in the field. The course is also suitable for those seeking an initial foray into research. By the conclusion of the course, students are expected to have developed a thorough understanding of personalized machine learning models and the practical skills and knowledge necessary for developing such models in research and commercial contexts. Moreover, it is expected that the course project should yield to a scientific paper (without the need of submission) or a practical solution that can be publicly shared and added to the student’s portfolio.