Introduction to Machine Learning in Design

Graduate

Spring With the recent blooming of artificial intelligence (AI) and machine learning (ML) came a renewed interest in how these technologies may impact architecture and other creative practices. Introduction to Machine Learning in Design introduces students to this emerging field, giving them the tools to make their own machine-learning-based design tools by adapting state-of-the-art models, developing new models, and understanding how data shapes machine learning algorithms. Throughout this course, students explore two main fields of machine learning and their potentials in design and making problems: Unsupervised Generative Models, Natural Language Processing. Students will be introduced to the fundamental concepts of each field and get hands-on experience with state-of-the-art research and tools to implement them. Prospective students should have strong Python programming skills. Prior basic familiarity with Machine Learning is a plus. Taking Fundamentals of Programming ( 15-112 ) or school of architecture courses such as Design + Computation 1 or Scripting and Parametric Design before taking this course is strongly recommended but not mandatory. Course Website: http://soa.cmu.edu