Presented By: Michigan Institute for Data and AI in Society MIDAS
AI for Scientists and Engineers Summer Academy
Academy Overview
The AI for Scientists and Engineers Summer Academy is designed for academic researchers, including university faculty, in a wide range of domains including biological sciences, engineering, environmental and earth science, physical sciences, and social sciences. Participants will learn the mathematical foundations of machine learning (ML), critically assess the data used in AI models, evaluate and validate ML model outputs, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics; prior coding experience is not required. Specific topics include supervised and unsupervised learning, neural networks, causal inference, and science-informed machine learning models.
The Summer Academy consists of three weeks of instructions, with different focuses. One can choose to attend any or all weeks; however, weeks 2 and 3 require some prior knowledge of AI / ML.
Week 1 (Monday, July 13 – Friday, July 17, 2026): The conceptual understanding of AI and its applications in domain research.
Week 2 (Monday, July 20 – Friday, July 24, 2026): The implementation of ML models in a Python environment.
Week 3 (Monday, July 27 – Friday, July 31, 2026): Advanced topics of AI and its applications in domain research.
The AI for Scientists and Engineers Summer Academy is designed for academic researchers, including university faculty, in a wide range of domains including biological sciences, engineering, environmental and earth science, physical sciences, and social sciences. Participants will learn the mathematical foundations of machine learning (ML), critically assess the data used in AI models, evaluate and validate ML model outputs, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics; prior coding experience is not required. Specific topics include supervised and unsupervised learning, neural networks, causal inference, and science-informed machine learning models.
The Summer Academy consists of three weeks of instructions, with different focuses. One can choose to attend any or all weeks; however, weeks 2 and 3 require some prior knowledge of AI / ML.
Week 1 (Monday, July 13 – Friday, July 17, 2026): The conceptual understanding of AI and its applications in domain research.
Week 2 (Monday, July 20 – Friday, July 24, 2026): The implementation of ML models in a Python environment.
Week 3 (Monday, July 27 – Friday, July 31, 2026): Advanced topics of AI and its applications in domain research.
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