Project Summary In the era of big data, expertise in advanced statistical analyses including machine learning (ML) and artificial intelligence (AI) is increasingly important for application to biological questions. In some cases, these approaches can be applied by the biologist themselves, but in many cases collaboration between biological researchers and ML/AI experts is needed. For these collaborations to be successful, streamlined communication between experts in each field is essential. To accomplish this optimized communication goal, biologists need a foundational understanding of ML/AI algorithms, how they can be appropriately applied to biological datasets, and how biological experiments need to be designed for these ML/AI approaches. Here we propose an intensive three-week workshop designed to teach trainees the fundamentals of ML/AI applications to biological data. This workshop will synergize well with other existing courses, workshops and trainings developed by The University of Oregon Presidential Initiative in Data Science that will also be available to trainees interested in expanded trainings in ML/AI. The workshop will combine lecture components, discussions of recent peer-reviewed literature, and hands on experience working with real data to train and apply ML/AI algorithms. Week one will cover necessary fundamental topics including (1) What is machine learning and Artificial Intelligence? (2) What are the most common algorithms underlying ML/AI analyses? (3) What kind of data do I need to apply ML/AI? (4) How can I handle large datasets from repositories for data mining and how can I make my data available on these databases? Lectures on fundamental ML/AI concepts will be intermixed with the basics of data manipulation and principals of FAIR (Findable, Accessible, Interoperable, and Reusable) data management. Week two will cover common ways data from various next generation sequencing technologies - including single-cell sequencing data - can be analyzed using ML/AI and will include hands-on training. Week three will focus on applying ML/AI to image analysis, including training on the analysis and annotation of image data, manipulating and transforming image files, and training a neural network image classifier to automate lab processes. By the end of the workshop, trainees will have the foundational skills needed to collaborate with ML/AI experts, ask new research questions about existing data and design powerful experiments, and explore novel research directions through applications of ML and AI.