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  • 33 467: Astrophysics of Stars and the Galaxy

    9.00 Credits
    Carnegie Mellon University

    The physics of stars is introduced from first principles, leading from star formation to nuclear fusion to late stellar evolution and the end points of stars: white dwarfs, neutron stars and black holes. The theory of stellar structure and evolution is elegant and impressively powerful, bringing together all branches of physics to predict the life cycles of the stars. The basic physical processes in the interstellar medium will also be described, and the role of multi-wavelength astronomy will be used to illustrate our understanding of the structure of the Milky Way Galaxy, from the massive black hole at the center to the halo of dark matter which emcompasses it.
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  • 33 499: Supervised Reading

    1.00 - 12.00 Credits
    Carnegie Mellon University

    The student explores a certain area of advanced physics under the supervision of a faculty member. The student must contact a faculty member and the Assistant Head for Undergraduate Affairs before registering.
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  • 33 650: General Relativity

    9.00 Credits
    Carnegie Mellon University

    General Relativity is the classical theory of gravity. It is widely recognized as a beautiful theory - equating gravity and the geometry of spacetime leads to a profound conceptual change in the way we regard the universe. The predictions of the theory are relevant to systems as varied as high precision measurements of the earth's gravitational field or the strongly curved space-times around black holes. In this course, we will gradually develop an understanding of the geometries which are the solutions of the Einstein equation, with an emphasis on their relevance to physical situations. We will motivate the theory step by step and eventually introduce the Einstein equation itself. Typical Textbook(s): "Gravity, An Introduction to Einstein's General Relativity" by James Hartle.
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  • 36 050: Study Abroad

    0.00 Credits
    Carnegie Mellon University

    No course description available.
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  • 36 051: Study Abroad

    0.00 Credits
    Carnegie Mellon University

    No course description available.
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  • 36 149: Statistics Freshman Seminar

    9.00 Credits
    Carnegie Mellon University

    Biomedical Imaging - What Can We See? Technological advances have made it possible to see inside a living animal, say, a human, and study its structure, and in some cases, its function. None of the imaging methods work like a traditional camera with film. This course will look at the details of several of these imaging technologies to understand how the images are constructed and what spatial and temporal resolution is possible. We will consider some of the reasons why an image might not actually look like the target tissue and we will visit a few of the imaging facilities to see the equipment in action. Specific technologies we'll consider include digital cameras and video, Xray, MRI, PET, and MEG, and may include NIRS, CT, TPLSM, and SPECT.
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  • 36 198: Research Training: Statistics

    9.00 Credits
    Carnegie Mellon University

    This course is part of a set of 100-level courses offered by H SS departments as independent studies for second-semester freshman, and first- or second-semester sophomores, in the College. In general, these courses are offered only as electives; i.e., they cannot be applied toward a college or major requirement, although the units do count toward graduation as ?elective? units. Additional details (including a roster and descriptions of Research Training Courses available in any given semester) are available in the H SS Academic Advisory Center. Prerequisites/restrictions: For H SS students only; only for second- semester freshmen, or first- or second- semester sophomores; minimum cumulative QPA of 3.0 (at the time of registration) required for approved entry; additional prerequisites (e.g. language proficiency) may arise out of the particular demands of the research project in question.
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  • 36 201: Statistical Reasoning and Practice

    9.00 Credits
    Carnegie Mellon University

    This course will introduce students to the basic concepts, logic, and issues involved in statistical reasoning, as well as basic statistical methods used to analyze data and evaluate studies. The major topics to be covered include methods for exploratory data analysis, an introduction to research methods, elementary probability, and methods for statistical inference. The objectives of this course are to help students develop a critical approach to the evaluation of study designs, data and results, and to develop skills in the application of basic statistical methods in empirical research. An important feature of the course will be the use of the computer to facilitate the understanding of important statistical ideas and for the implementation of data analysis. In addition to three lectures a week, students will attend a computer lab once a week. Examples will be drawn from areas of applications of particular interest to H SS students. Not open to students who have received credit for 36-207/70-207, 36-220, 36-225, 36-625, or 36-247.
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  • 36 202: Statistical Methods

    9.00 Credits
    Carnegie Mellon University

    This course builds on the principles and methods of statistical reasoning developed in 36-201 (or its equivalents). The course covers simple and multiple regression, analysis of variance methods and logistic regression. Other topics may include non-parametric methods and probability models, as time permits. The objectives of this course is to develop the skills of applying the basic principles and methods that underlie statistical practice and empirical research. In addition to three lectures a week, students attend a computer lab once week for "hands-on" practice of the material covered in lecture. Not open to students who have received credit for: 36-208/70-208, 36-309.
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  • 36 207: Probability and Statistics for Business Applications

    9.00 Credits
    Carnegie Mellon University

    This is the first half of a year long sequence in basic statistical methods that are used in business and management. Topics include exploratory and descriptive techniques, probability theory, statistical inference in simple settings, basic categorical analysis, and statistical methods for quality control. Not open to students who have received credit for 36-201, 36-220, 36-625, or 36-247. Cross-listed as 70-207.
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