Experiential, field-based course that seeks to ground students in the planted landscape and nurture a sense of place. Identification, morphology and uses of landscape plants including annuals, perennials, woody shrubs and trees, evergreens and groundcovers. Horticultural practices such as pruning, division, hybridizing, bulb planting, close observation, and design basics. Discussions will consider equity and access, local food systems, ecosystem services, urban greening, and climate/sustainability. Field trips (remote only in 2020) are an important component of the course.

Experiential, field-based course that seeks to ground students in the planted landscape and nurture a sense of place. Identification, morphology and uses of landscape plants including annuals, perennials, woody shrubs and trees, evergreens and groundcovers. Horticultural practices such as pruning, division, hybridizing, bulb planting, close observation, and design basics. Discussions will consider equity and access, local food systems, ecosystem services, urban greening, and climate/sustainability. Field trips (remote only in 2020) are an important component of the course.

This seminar will review domain literature concerning best practices in diversity, inclusion, and pedagogy, while connecting these topics to workplace and classroom experiences. Starting with core literature and examples from geosciences, geology, geography and elsewhere, students will engage in critical discussion of how race, gender, class and other identities have been marginalized in these fields. Through conversations, reflections, and participatory actions, this course will explore current issues and consider how to create an equitable landscape moving forward.

Over the last few years, a wave of excitement about machine learning (ML) and deep learning has proliferated from academia to industry, transforming prototypes in research labs to valid solutions to real-world problems. Using ML entails developing end-to-end pipelines to collect data, clean it, and run learning and inference algorithms in a scalable manner. This results in computationally intense workloads and complex software pipelines. Systems for ML help users organize their data and scale these computationally intense problems to larger and larger datasets.

This course explores the complex relationships between sickness and literature. We will conduct a wide-angle survey of writings about disease, considering both how outbreaks shaped literary history and how literature shaped cultural understandings of disease. Furthermore, we ask: how has the cultural meaning of "contagion" been refracted through discourses around race, gender, sexuality, and nationalism?