In the course "Machine Learning in Industry" doctoral candidates will explore the foundational principles of applying machine learning in real-world industrial settings in the financial domain. Throughout this course, candidates will learn about the key concepts and methodologies of machine learning, with a focus on their practical implications for businesses, as well as how to assess the potential impact of automation decisions on various aspects of business operations, including productivity, efficiency, and cost-effectiveness. Additionally, it covers topics such as the availability and costs of acquiring high-quality data, data preprocessing techniques, and strategies for managing data privacy and security concerns. By the end of this course, candidates will be equipped with the knowledge and tools needed to utilize machine learning to drive innovation, optimize business processes, and achieve strategic objectives in diverse financial industrial contexts.