09-15-2026, 07:44 PM
MIT’s Modeling: MultiModal AI (MMAI) course for Spring 2026 focuses on how AI can work with multiple types of data simultaneously rather than treating language, images, audio, sensors, medical information, music, and other modalities separately.
The course covers core techniques for representing and fusing heterogeneous data, aligning different views, performing multimodal reasoning, generating multimodal content, transferring knowledge between high- and low-resource domains, and developing safe, ethical, human-aligned AI systems. Its broader goal is to give students both technical knowledge of recent AI developments and the intuition needed to apply AI to unfamiliar combinations of data.
The course is strongly hands-on and research-oriented. Students attend two lectures per week and complete readings, homework, and intermediate practical assignments in which they apply multimodal AI to data and problems of their choice. The course culminates in a team-based, instructor-approved research project investigating a new data modality or a multimodal AI problem. In this sense, the course is not simply an introduction to existing AI models: it is designed to teach students how to think like AI researchers, formulate new problems, experiment with multimodal systems, and potentially create novel AI methods or applications.
COURSE LINK
The course covers core techniques for representing and fusing heterogeneous data, aligning different views, performing multimodal reasoning, generating multimodal content, transferring knowledge between high- and low-resource domains, and developing safe, ethical, human-aligned AI systems. Its broader goal is to give students both technical knowledge of recent AI developments and the intuition needed to apply AI to unfamiliar combinations of data.
The course is strongly hands-on and research-oriented. Students attend two lectures per week and complete readings, homework, and intermediate practical assignments in which they apply multimodal AI to data and problems of their choice. The course culminates in a team-based, instructor-approved research project investigating a new data modality or a multimodal AI problem. In this sense, the course is not simply an introduction to existing AI models: it is designed to teach students how to think like AI researchers, formulate new problems, experiment with multimodal systems, and potentially create novel AI methods or applications.
COURSE LINK
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