Advanced Topics in Multimodal Machine Learning

Graduate

Spring This course is designed to be a graduate-level course covering recent research papers in multimodal machine learning, a vibrant multi-disciplinary research field which studies modeling, alignment, and fusion of heterogeneous data from multiple modalities, including language, vision, and acoustic. The course will focus on discussions and understanding of recent research papers in this field. Students are expected to have already taken 11-777 Multimodal Machine Learning course or have equivalent research experience (instructor approval required). The course is planned for 6 credit units. Optionally, students can register for 12 credit units, with the expectation to do a comprehensive research project as part of the semester. These course projects are expected to be done in teams, with the research topic to be in the realm of multimodal machine learning and pre-approved by the course instructors. Prerequisite: 11-777 Min. grade C