Machine learning is a core technology of artificial intelligence (AI) that enables computers to act without being explicitly programmed. Recent advances in machine learning have given us, inter alia, self-driving cars, AlphaGo, Amazon, and Netflix. This technology has also allowed us to predict armed conflict and post-electoral violence, detect fake news, develop targeted provision of care and public services, and implement early policy interventions.
This course provides a hands-on introduction to machine learning and data science. We will cover topics in supervised and unsupervised learning, including regression, classification, random forests, clustering, and dimensionality reduction. You will learn the fundamental concepts underlying machine learning algorithms, but we will equally focus on the practical use of machine learning algorithms using open source libraries in R. The course will also discuss recent applications of machine learning in political science and public policy. The final project will allow the practical application of the learned concepts to real-world problem-solving.
Prerequisites: Statistics II (or equivalent)
This course is for 2nd year MIA and MPP students only.