Advanced study in a specialized field under the supervision of a member of the department staff. Before registering, the student must submit an outline of the proposed work for approval of the supervisor and the department chair.
Before registering, the student must submit an outline of the proposed work for approval by the supervisor and the chair of the Department. Advanced study in a specialized field under the supervision of a member of the department staff. May be repeated for credit.
Before registering, the student must submit an outline of the proposed work for approval by the supervisor and the chair of the Department. Advanced study in a specialized field under the supervision of a member of the department staff. May be repeated for credit.
Prerequisites: the instructors permission. Individual research and tutorial in archaeology for advanced graduate students.
Prerequisites: the instructors permission. Individual research and tutorial in archaeology for advanced graduate students.
Guided reading and research on a topic or in a field chosen by the student in consultation with a member of the faculty.
The biostatistical field is changing with new directions emerging constantly. Doing research in these new directions, which often involve large data and complex designs, requires advanced probability and statistics tools. The purpose of this new course is to collect these important probability methods and present them in a way that is friendly to a biostatistics audience. This course is designed for PhD students in Biostatistics. Its primary objective is to help the students achieve a solid understanding of these probability methods and develop strong analytical skills that are necessary for conducting methodological research in modern biostatistics. At the completion of this course, the students will a) have a working knowledge in Law of Large Numbers, Central Limit Theorems, martingale theory, Brownian motions, weak convergence, empirical process, and Markov chain theory; b) be able to understand the biostatistical literature that involves such methods; c) be able to do proofs that call for such knowledge.
Prerequisites: the instructors permission. Individual research in all divisions of anthropology and in allied fields for advanced graduate students
This course offers a general introduction to essential materials in advanced statistical theory for doctoral students in biostatistics. The course is designed to prepare doctoral students in biostatistics for their written theory qualifying exam. Students in this course will learn theory of estimation, confidence sets and hypothesis testing. Specific topics include a quick review of measure-theoretic probability theory, concepts of sufficiency and completeness, unbiased estimation (UMVUE), least squares principle, likelihood estimation, a variety of estimators and their asymptotic properties, confidence sets, the Neyman-Pearson lemma and uniformly most powerful tests. If time permits, the likelihood ratio test, score test and Wald test, and sequential analysis will be covered.
An internship arranged through the Museum Anthropology program of 10 hrs/week (for 3 credits) or 20 hrs/week (for 6). Involves meaningful work, requires keeping a journal and writing a paper at the completion of the semester. Not to be taken without permission of the program directors, usually after completing the Museum Anthropology core courses.
The course is designed for entering doctoral students and provides a rigorous introduction to the fundamental theory of optimization. It examines optimization theory in continuous, deterministic settings, including optimization in Euclidean as well as in more general, infinite-dimensional vector spaces. The course emphasizes unifying themes (such as optimality conditions, Lagrange multipliers, convexity, duality) that are common to all of these areas of mathematical optimization. Applications across a range of problem areas serve to illustrate and motivate the theory that is developed. Additionally, review sessions explaining how to solve complex optimization problems using CVX and Python are offered.
The aim of this course is to provide students a systematic training in key topics in modern supervised statistical learning and data mining. For the most part, the focus will remain on a theoretically sound understanding of the methods (learning algorithms) and their applications in complex data analysis, rather than proving technical theorems. Applications of the statistical learning and data mining tools in biomedical and health sciences will be highlighted.
FILM AF 9120 TV Revision
The goal of TV Revision is to bring in a completed pilot and then completely revise it in one semester. Students will initially present their full scripts for feedback in class discussion, then map a plan for rewriting with their instructor. Deadlines throughout the semester will focus on delivery of revised pages.
The work can range from an intensive page 1 rewrite to focus on selected areas in a script. Reading of all scripts in the workshop and participation in class discussion is required.
There is an application process to select students for the class.
This is an advanced course for first-year Ph.D. students in Biostatistics. The aim is to provide a solid foundation of the theory behind linear models and generalized linear models. More emphasis will be placed on concepts and theory with mathematical rigor. Topics covered including linear regression models, logistic regression models, generalized linear regression models and methods for the analysis contingency tables.
This course is designed to increase student knowledge of Psychiatric Nurse Practitioner Case Narrative writing, and DNP competencies. Students will use case narratives as a framework to synthesize knowledge, assessment, and clinical thinking skills. Students will explore Social Justice competency, Competency D3C3 in depth. Students will develop a treatment intervention that identify and challenge biases that contribute to health disparities.
This fourteen-week elective, open to Research Arts Screen & TV Writing students, will provide vital directing advisement to Portfolio films. It covers pre-production and director’s prep.