In this course, our primary aim is to delve deep into advanced data analysis methods, enabling you to handle vast and unstructured datasets with confidence and expertise.

Throughout this journey, you will gain a comprehensive understanding of cutting-edge techniques that extend beyond basic predictions. We emphasize obtaining statistically valid inferences on parameters, hypotheses, probabilistic forecasts, and measures of uncertainty, with Bayesian methodology serving as a central framework for probabilistic machine learning. The course strikes a balance between theory and practical applications, ensuring you not only know how to apply these methods but also understand the fundamental principles behind them.

Bridge the gap between data and actionable insights with this executive education course

Our course modules will guide you through foundational concepts in Bayesian statistics and computational methods, high-dimensional statistics, probabilistic machine learning, causal inference, and the analysis of unstructured data. You will also become proficient in modern computational tools and software, allowing you to effectively deploy these methods in real-world scenarios. While we place a strong emphasis on applications in Economics and the Social Sciences, the knowledge you gain here has broad relevance, and we will showcase examples from various fields and disciplines such as Biomedicine.

By the end of this course, you will have a deep understanding of statistical machine-learning methods and their applications. You will be well-equipped to tackle complex data analysis tasks and make informed decisions in various fields. 

Apply your knowledge through real-world case studies, gaining practical experience in implementing techniques

Course Learning Methods
 

• Lectures and Presentations: Engaging lectures and presentations will provide you with a strong foundational understanding of the methodology used in statistical machine learning for large and unstructured data. We'll delve into the theory underpinning these methods to ensure you not only know how to apply them but also understand why they work.
• Foundational Topics: Begin with a foundational lecture on Bayesian statistics and computational methods, followed by an in-depth exploration of high-dimensional statistics, probabilistic machine learning, treatment effect estimation, and latent discrete variable models.
• Hands-On Application: Introduce modern computational methods and programming software, enabling efficient deployment of the discussed methods.
• Interdisciplinary Approach: While we emphasize applications in Economics and the Social Sciences, these principles extend to diverse fields. Explore examples from disciplines such as Biomedicine, broadening your understanding of the versatile applications of these methods.