View source for Spring 2012 EN.530.660

<big> '''Computational Analysis of Stochastic Processes (EN.530.660)''' </big>

''Note: this page will remain under construction until the first day of class.''

'''Instructor:''' <big> [http://limbs.lcsr.jhu.edu/User:Scarver Sean G. Carver, Ph.D.]</big>, Hackerman 128, The Department of Mechanical Engineering, The Johns Hopkins University.

'''Semester Offered:''' Spring 2012.

'''Class:''' Meets Monday, Wednesday, Friday, 4:30--5:20 pm in [Homewood Campus, ROOM TO BE ANNOUNCED]

'''Spring Break:'''  March 19, 21, and 23.  No class.

'''Class website:''' [http://www.seancarver.org/ http://www.seancarver.org]

'''One Hundred Word Description:'''  This class will cover stochastic processes (including both discrete and continuous time, and including both discrete and continuous state), leading to a rigorous treatment of stochastic differential equations and filtering, emphasizing computation. The class will draw from examples relevant to engineering, such as the Kalman filter. The course will comprehensively, but rapidly review all needed material in probability and statistics. 

[[Stochastic_Processes_Syllabus|'''Tentative Syllabus: (click here)''']]

'''Prerequisites:''' Undergraduate probability theory (e.g. 550.420) and graduate linear systems theory (e.g. 530.616), or permission of instructor.

'''Office hours:''' By appointment. I will generally keep the hour after class free.  To arrange an appointment, see me after class or send an email to [mailto:seancarverphd@gmail.com seancarverphd@gmail.com].

'''Expectations:''' Class attendance and homework.  Additionally, there will be two learning assessments made during the semester: a mid-term and a final.  The mid-term assessment will be a take home exam on the first two chapters of the book.  The final assessment will be a collaborative project.

'''Final Grade:'''  Based 50% on homework, 25% on mid-term assessment, 25% on final assessment.

'''Lecture etiquette:''' If I say something you do not understand, stop me and ask me to explain it again. Some of the lectures will be challenging and I insist on taking the time necessary to make sure everyone understands the material.

'''Textbook:''' ''Numerical Solution of Stochastic Differential Equations''.  By Peter E. Kloeden and Eckhard Platen.  Springer.

'''Academic integrity:''' You are expected to make each homework assignment a good learning experience, enforced by the honor system.  You can discuss homework, and give and receive hints to and from other students on homework, but after getting help, do the assignments yourself and hand in your own work.  The textbook has solutions to its problems in the appendix.  For textbook problems, (other problems will come from elsewhere), the obvious caveats apply: peek at the answer only after you wrestle with the problem first.  Don't copy the answer!  The textbook answers are terse.  If you fail to find the answer on your own, then it will suffice if you explain each step in the textbook solution.  (But please try the problem on your own first.)  The midterm assessment will be a take home exam.  Unlike for the homework sets, for the take home exam, no interaction with other students is permitted.  The final assessment will be a project, interaction with other students is not only permitted, but encouraged.  But give due credit to those who have substantially helped you.

'''Experimental teaching methods and surveys'''.  I will be experimenting with two different technology based teaching methods that have created myself.  I call them [[Kindle_In_The_Classroom|Kindle In The Classroom]] and [[Mediawiki_In_The_Classroom|Mediawiki_In_The_Classroom]].