English

This course uses key environmental debates to explore the challenges associated with the effective management of the environment. Environmental problems are complex and their resolution involves integrating science, knowledge, and values. Course topics are divided into five sections: environmental science, global marine issues, global water issues, global terrestrial issues, and global climate issues.

This course looks at how one's voice is made and how one uses and modulates it. The course provides understanding of the physiological processes that produce voice and the relationship between mind and body in vocal communication. This practical workshop uses techniques developed by actors and singers that improve the resonance and musicality of the speaking voice and also vocal strength and endurance. Using verse, prose and dramatic text, students work on vocal characteristics - pitch, intonation patterns, pace and pausing, placement to improve their oral delivery.

This course is a broad introduction to computer vision for data science. Topics include various low-level image processing methods and high-level vision tasks like image classification and object detection, with modern approaches based on deep learning. Student learn computer vision algorithms and implement them in python. Students should be proficient in python programming with numpy for assignments. Other python libraries, such as opencv (cv2), scipy, and matplotlib will be used, but students do not have to be proficient with them.

This course explores the political, social, economic, and cultural aspects of the complex idea of home. Major topics include: sense of place, home technologies and design, gender and housework, home and travel, globalisation, nationalism, homelessness, exile, and representations of home. Students complete the course with a new appreciation for the complexity of the places – house, neighborhood, nation, planet – they call home.

The goal of this course is in introducing popular skills for analyzing economic data. We attempt to achieve this goal by getting familiar with the well-known econometric analyses and linking this to the knowledge on the numerical outputs generated by standard statistical packages. In attaining this goal, our interests will be focused more on cross-sectional data and their slight extensions. There are two reasons for this focus. First, analysis of cross-sectional data is a building bloc for the analysis of many other data sets. Second, the analysis of cross-sectional data is easier than analyzing other data sets as they do not involve too much complication that comes from the variation assumptions. Eventually, by these, studying cross-sectional data becomes a good starting point for achieving the specified objectives, even though their applicability is not so limited. After completing this course, students are expected to be able to conduct the following: Understanding the implicit assumptions behind economic data analysis; Interpreting the numerical outputs generated by standard statistical packages.

Prerequisite: Mathematics for economics and statistics; Recommended: Mathematical statistics.

This course examines the literature of wisdom, both ancient and modern, and looks at how reading literature can deepen, enrich, and improve one's life in modern society.