Statistical Learning

Machine learning (ML) has enjoyed tremendous successes in a wide variety of applications. Most notable are computer vision and natural language processing. Those accomplishments are thanks to its solid foundations in statistics, computer science and mathematics. The next frontier for ML is business and/or finance where ML’s applications are in an early stage. Successful ML integrations into business domain require a rigorous understanding of the foundations of ML so that appropriate models can be adapted to real-world applications with a careful consideration of costs and risks involved. This statistical learning course takes you on a journey from classical statistics to statistical learning theory. You will start with basic ideas originated from statistics, understand the strengths and weaknesses of classical models and develop the learning algorithms addressing those weaknesses. The course will build you a strong background to move on to the next stage in ML with more advanced courses or to create real-world applications.