Machine Learning for All

Catalog description

This course introduces the fundamentals of machine learning (ML) with a focus on applications in social sciences. Students will learn key concepts, methods, and tools for analyzing and interpreting data, enabling them to apply machine learning techniques to real-world social science problems. Key ML algorithms like learned decision trees, logistic regression, artificial neural networks will be introduced in this course. Students will carry out ML experiments with real datasets. The hands-on experiments will use low code software like Orange Data Mining, Weka, KNIME and Blockly-DS to increase student understanding of the typical elements of the ML workflow. Equipped with this understanding, the concepts of the Python programming language and supporting tools like programming notebooks and ML libraries in Python will be introduced so students are exposed to how ML software is developed in the industry.