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Since the program is interdisciplinary, students may be unfamiliar with one or more of the core disciplines. One of our primary concerns is responding to the educational needs of students whose primary undergraduate training was in very different areas. We address this problem on two fronts: core courses and interdisciplinary courses. See a sampling of our courses. In addition to coursework, graduate students must satisfy three research milestones and teacher training. Instruction is also enriched by retreats and seminars . The following table provides an overview of the graduate student requirements with a timeline. The requirements noted here are additions to, or modifications of, the requirements of the each of the home departments. Table 1
Take a look at sample programs of study for three hypothetical students.
We offer a set of core courses at the graduate level which provide an introduction to the respective fields without presupposing an extensive background. Graduate students are required to take the core courses in each of the two non-home departments and are encouraged to do so in their first year. Secondly, Brown offers specialized complementary or joint courses that span disciplinary and departmental boundaries. Students are required to take at least three of these courses during their graduate careers. At least one such seminar would be collaboratively taught each year by the CMM postdoctoral fellows. Also, each department typically offers several new seminars a year, an important component of advanced graduate student training. Brown actively encourages exploration and innovation in teaching, and our teaching loads permit us to develop new courses on important topics. New courses are generally first given a "trial run" as special seminars and the most successful among them are incorporated into the regular teaching schedule. CG220 Core Topics in Cognitive Science &core course covering essential background and current issues in cognitive science, including experimental, computational, and neurophysiological perspectives. Three topics (cognition, perception, and language) rotate yearly. AM193 Computational Probability and Statistics – This looks at the role of computing in modeling and analyzing complex stochastic systems. Topics motivated by applications in the Computer, Cognitive, and Neural Sciences. In each topic area students will perform computer experiments (in Matlab) that highlight and confirm the analytic foundations developed in class. Prerequisites: basic knowledge of probability, statistics, and linear algebra. AM195 Computational Probability and Statistics II – This will be a continuation of AM193, intended to introduce mathematical foundations and techniques of current interest in the neural, cognitive, and computer sciences. Possible topics include: Monte Carlo computational methods; the EM algorithm; probabilistic grammars; mean field theory; information theory (entropy, Kullback-Leibler "distance," probabilities and codes); Poisson processes; ordinary and partial differential equations in neural modeling. CS141 Introduction to Artificial Intelligence – This covers theoretical and practical issues in getting systems to perform tasks that require intelligence. Example tasks range from scheduling airlines and controlling factories to cleaning floors and delivering mail. Topics include methods for representing complex real-world knowledge, learning from examples or reinforcement, planning and diagnostic reasoning, understanding natural-language text, robotics, and machine vision. CS148 Building Intelligent Robots – This addresses the problem of controlling physical systems that operate in dynamic, unpredictable environments. Students, in pairs, build their own mobile robot and program it to perform a variety of simple tasks. Also examines the major paradigms of robot programming and studies architectures for building perception and control systems for intelligent robots. CS195 Probabilistic Methods in Computer Science – This looks at advanced algorithms, focusing on the use of probabilistic techniques in the design and analysis of algorithms. The basic tools from probability theory and probabilistic analysis that are recurrent in algorithmic applications, together with a variety of algorithmic applications of these techniques. CS241 Statistical Models in Natural Language Understanding – This covers various topics in computer understanding of natural language, primarily from a statistical point of view. Topics include: entropy, hidden Markov models, word-tagging models, probabilistic context-free grammar induction, syntactic disambiguation, semantic word clustering, and word-sense disambiguation. There are three research milestones for CMM graduate students: • A first- or second-year research project. Graduate students are expected to organize an interdisciplinary committee and to find a topic for this project by the end of their first year. The project should be of the magnitude of a strong masters thesis and should culminate in a publishable (or near-publishable) research paper and presentation. Successful completion of this project, in addition to the course requirements of the individual department, will result in the student's admission to candidacy for the Ph.D. degree. • Identifying and developing a topic for a dissertation. This milestone takes place in the third year, when graduate students perform basic background research in their areas. The research culminates in a preliminary paper for students whose home department is Cognitive and Linguistic Sciences, in a thesis proposal for students in Computer Science, and a thesis proposal presentation for students in Applied Mathematics. These papers and proposals are presented at a retreat for general comment and committee approval. We consider it especially important that students present their research in an interdisciplinary setting. We want our students to be capable of communicating their research to scientists who are not specialists in their particular field, and the retreats play a crucial role in developing that ability. The retreats are also used to present seminars on the appropriate conduct of professional activities such as grant preparation and the ethical aspects of scientific research. In addition, all students engaging in behavioral research must complete a laboratory course in Cognitive Science, in which effective research design and the ethics of research with human subjects are discussed. • A dissertation. The dissertation is typically related to the topic the student researched and proposed in the third year, and supervised by the same interdisciplinary committee. Standard time to completion of the degree is four to five years. Postdoctoral fellows and graduate students receive preparation for university teaching in several ways: • They participate in a series of workshops, offered by the Brown University Harriet Sheridan Center for the Advancement of College Teaching, and aimed at introducing classroom teaching methods and addressing concerns faced by new teachers. • They give presentations in graduate seminars, research talks in the CMM retreats and colloquia, and a formal presentation of the 1st/2nd year research project and the preliminary project. • They receive experience in one-on-one research mentoring by serving as research advisors for undergraduate researchers. Graduate students mentor undergraduates with the supervision of faculty. • Additionally, graduate students serve as teaching assistants for two-to-four semesters (as required by the home department), working closely with faculty members to prepare large-enrollment or laboratory courses, lead laboratory or discussion sections, and deliver at least one class lecture each term that is critiqued by a faculty member. Individual graduate trajectories are designed to interweave research, coursework, and teacher training into effective and innovative instructional programs. Each individual program will have a clear interdisciplinary topical focus. Below are potential programs of study for three hypothetical graduate students – one each in Applied Mathematics, Computer Science, and Cognitive and Linguistic Sciences. Example 1: A student interested in vision. Home department: Applied Mathematics. First year Second year Third Year Example 2: A student interested in human and robotic navigation. Home department: Computer Science. First year Second year Third year Example 3: A student interested in computational models of language learning. Home department: Cognitive & Linguistic Sciences. First year Second year Third year |
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