ST- Pedestrians and Bicyclists

This course covers operations and safety topics related to bicycle and pedestrian transportation. Bicycle and pedestrian infrastructure treatments, complete streets, and the impacts of such designs and practices on operations and safety are studied. In addition, this course includes topics related to multimodal signal control and level of service as well as the connection between bicycle and pedestrian infrastructure with physical activity and overall health outcomes. Discussion on how bicycling and walking are accounted for in decision-making is also included.

Field Ecology: Exp Approach

This course provides an introduction to methods in field ecology, with an emphasis on rigorous experimental design, hypothesis testing, data collection, introductory data analysis, and presenting results. The ability to pose clear questions, state hypotheses, and design appropriate experiments to test these hypotheses is of fundamental importance in all research disciplines; this course takes advantages of challenges in field ecology to address these essential topics.

Field Ecology: Exp Approach

This course provides an introduction to methods in field ecology, with an emphasis on rigorous experimental design, hypothesis testing, data collection, introductory data analysis, and presenting results. The ability to pose clear questions, state hypotheses, and design appropriate experiments to test these hypotheses is of fundamental importance in all research disciplines; this course takes advantages of challenges in field ecology to address these essential topics.

ST- Genomics and Data Science

This course provides an introduction to genomics, bioinformatics and data sciences skills. Computer-based lab sessions will provide hands-on training in data science skills (Unix command line, Python, R, reproducible research, and cluster computing) and we will use them to learn bioinformatic methods related to gene expression, detecting variation, genome visualization, and critical statistical methods to understand large-scale datasets. The final project will be data analysis of the student's choice.

Field Ecology: Exp Approach

This course provides an introduction to methods in field ecology, with an emphasis on rigorous experimental design, hypothesis testing, data collection, introductory data analysis, and presenting results. The ability to pose clear questions, state hypotheses, and design appropriate experiments to test these hypotheses is of fundamental importance in all research disciplines; this course takes advantages of challenges in field ecology to address these essential topics.

Self&Ethcs/Great Books of Asia

What does it mean to be human self? Where do men and women ?fit? in the universe? What is the good life? What is evil? In this course we will discover how these questions are addressed in the Great Books of three civilizations of Asia: Persia, India and China. We will seek a deep engagement with Confucian, Taoist, Buddhist, Hindu, and Islamic-Sufi thinkers in a conversation between cultures and times about the fundamental questions of self and ethics. (Gen. Ed. AL)

The Scientific Mind

In thiscourse, taught in English, students will explore how the concept of the scientific mind develop in German sciences, literature, art, and philosophy from the eighteenth century to the present. By examining parallel and intersecting developments in cultural products and in the natural sciences, we will examine how knowledge was separated into different fields.

ST- Literature and Migration

Focusing on a variety of literary genres this course will explore aesthetic representations of and engagements with migration, as well as texts born of migration. In addition to primary sources, course materials include scholarship on the labeling, classification and positioning of these texts, their public-political interventions and literary (re)conceptualizations of home, belonging, refuge, citizenship, exile, and migration.

ST-HardwareDes/MachLrngSyst

Study architectural techniques for efficient hardware design for machine learning (ML) systems including training and inference. Course has three parts. First part deal with ML algorithms: regression, support vector machines, decision tree, and naive Bayes approaches. Second part deals with convolutional and deep neural network models.
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