Ethical questions about museum activities are legion, yet they are usually only discussed when they become headlines in newspapers. At the same time, people working in museums make decisions with ethical and legal issues regularly and seldom give these judgments even little thought. In part, this is due to the fact that many of these decisions are based upon values that become second nature. This course will explore ethical issues that arise in all areas of a museum's operations from governance and management to collections acquisition, conservation, and deaccessioning. We will also examine the issues that arise when the ownership of objects in a museum's are questioned and the ethical considerations involved in retention, restitution and repatriation.
This course introduces students to a range of obstacles that have arisen - and continue to arise - in the struggle to make sure that women are treated as full and legitimate bearers of human rights as well as some of the significant critiques that have emerged from this struggle. The course provides a historical overview of conflicts over women's roles in family, the economy and the body politic and addresses gains women have made as well as challenges they face in relation to economic development, military conflict, domestic inequality, health, and religious and cultural beliefs. Materials provide a range of comparative views of advances and obstacles to women's rights in Latin America, Asia, Africa, Europe and the U.S. Students will also learn about significant instruments, strategies, and movements intended to remedy the inequalities that affect women.
Advanced introduction to classical sentential and predicate logic. No previous acquaintance with logic is required; nonetheless a willingness to master technicalities and to work at a certain level of abstraction is desirable. Note: Due to significant overlap, students may receive credit for only one of the following three courses:
PHIL UN3411
,
UN3415
,
GR5415
.
Prerequisites: all 6 MAFN core courses, at least 6 credits of approved electives, and the instructor's permission. See the MAFN website for details.
This course provides an opportunity for MAFN students to engage in off-campus internships for academic credit that counts towards the degree. Graded by letter grade. Students need to secure an internship and get it approved by the instructor.
This course explores mobility – past and present – as an object of anthropological analysis, through mix of theoretical texts and ethnographic and archaeological case studies. In it, mobility is approached as an analytical object in two ways. First, it examines how mobility is structured in/through space, time, scale, as well as by landscapes, infrastructures, companion species, subjectivities, and ideologies. Second, this course engages with the ways in which mobility has structured anthropological understandings of societies and their history. As part of this, it interrogates the development of mobility studies and the arguments for novel mobilities in the contemporary world.
Prerequisites: Calculus
This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.
Prerequisites: working knowledge of calculus and linear algebra (vectors and matrices), and STAT GR5203 or the equivalent.
In this course, we will systematically cover fundamentals of statistical inference and testing, and give an introduction to statistical modeling. The first half of the course will be focused on inference and teesting, covering topics such as maximum likelihood estimates, hypothesis testing, likelihood ratio test, Bayesian inference, etc. The second half of the course will provide introduction to statistical modeling via introductory lectures on linear regression models, generalized linear regression models, nonparametric regression. and statistical computing. Throughpout the course, real-data examples will be used in lecture discussion and homework problems. This course lays the foundation, preparing the MA in Data Science studnets, for other courses in machine learning, data mining and visualization.
Incoming M.A. students aiming for the thesis-based program are guided through the process of defining a research question, finding an advisor, and preparing a research proposal. By the end of the semester the students will have a written research proposal to submit to potential advisors for revision. Subject to a positive review of the research proposal, students are allowed to continue with the thesis-based program and will start working with their advisor. The course will also provide an opportunity to develop basic skills that will facilitate the reminder of the student's stay at E3B and will help in their future careers.