The course will consist of applying machine learning techniques for extracting domain insights from real-world or simulated datasets. It will take the form of multiple lab exercises in Python, where the students will extract data, apply visualization techniques, train machine learning models on this data, use model selection/validation techniques, and finally apply interpretability techniques to extract domain insights from the learned models.

Recommended prior course: Machine Learning for Data Science

Format: Oral exam at the end of the semester