ASTR 496

Fall 2011 All Classes

All Classes

Seminar in Astronomy

Credit: 1 TO 4 hours.

Lectures on topics of current interest in astronomy and astrophysics; for advanced undergraduates and graduates. See Class Schedule for current topics.

Approved for both letter and S/U grading. May be repeated. Prerequisite: Consent of instructor.

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ASTR 496 class schedule data for fall 2011
	CRN	Type	Section	Time	Day	Location	Instructor	Section Details
	58290	Lecture	ISM	11:00AM -12:20PM	MW	134 Astronomy Building	Chu, Y	Part of Term: 1 Date Range: 08/22/11-12/07/11 Credit: 4 hours Section Title: Interstellar Medium Section Info: InterStellar Medium: 4 credit hours In this course, students will learn practical knowledge of the interstellar medium (ISM) and skills to carry out observational research and write papers and grant proposals. Lectures will be given on selected topics of the ISM. Students are required to use archival X-ray (Chandra, XMM, and ROSAT), UV (IUE, FUSE, HST STIS/COS), optical (HST), IR (Spitzer, Herschel, HST), gamma-ray (Fermi) data to study specific problems, present the results in a publishable paper, and/or write a proposal requesting funding for the research. There will be homeworks, a mid-term oral presentation of their research progess, and a final paper reporting the research results. x 596
	58455	Lecture	PI	11:00AM -12:20PM	T	134 Astronomy Building	Brunner, R	Part of Term: 1 Date Range: 08/22/11-12/07/11 Credit: 2 hours Section Title: Practical Informatics Section Info: Practical Informatics for the Physical Sciences Description: Many scientific disciplines are becoming data rich, requiring new skills and approaches. This course will cover many of the topics required to effectively work with large data sets including: storage, analysis, visualization, and mining. Homework problems will reinforce the techniques introduced in lecture, some of which will require basic programming in the Python language. Specific topics to be covered include database technology, SQL, Bayesian statistics, hypothesis testing, parameter estimation, model fitting, Fisher matrices, classification, decision trees, neural networks, times series analysis, and spatial statistics. Meets: Tuesdays 11:00 am - 12:20 pm. Grading: S/U 2 credit hours x with 596