Foundations of Machine Learning with Applications in Python

Research, policymaking, and business rely on ever-bigger data to answer wide-ranging questions. What are the risk factors for developing a disease? How to assess the risk profile of a new customer, when determining the appropriate insurance premium? How to best forecast unemployment? How to optimally target online advertisements? Machine-learning techniques are well-suited to answer such data-driven questions.

In this course, we provide a fast-paced and solution-oriented introduction to machine-learning algorithms. Special attention is paid to the theoretical foundations of machine-learning algorithms, as well as real-life applications.

During the lectures, we will introduce you to a wide variety of machine-learning techniques, ranging from linear and nonlinear regression models to dimensionality-reduction techniques and clustering methods, as well as deep learning using neural networks.

During the lab sessions, we will guide you step by step through real-life case studies in economics, business, and medicine. We discuss how to implement machine-learning solutions, from conceptualizing the problem and implementing the appropriate techniques in Python, to evaluating the quality of your solution and ensuring its scalability, as well as overcoming challenges such as overfitting