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README.md

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 #Practical Numerical Methods with Python
 
 A multi-campus, connected course (plus MOOC) on numerical methods for differential equations in science and engineering. Collaboratively developed by:
-- Lorena A. Barba, George Washington University, USA
+- [Lorena A. Barba](http://lorenabarba.com), George Washington University, USA
 - Ian Hawke, Southampton University, UK
 - Carlos Jerez, Pontificia Universidad Catolica de Chile
 
 **Note:** David Ketcheson, from King Abdullah University of Science and Technology (KAUST), Saudi Arabia was going to be our fourth partner, but unfortunately the local course at KAUST got cancelled due to low enrollment.
 
-[**"Practical Numerical Methods with Python"**](http://openedx.seas.gwu.edu/courses/GW/MAE6286/2014_fall/about) is an open, online course hosted on an independent installation of the Open edX software platform for MOOCs.
-The MOOC (massive open online course) is ran by the George Washington University, while each instructor is running a local course, for credit at their institution. You can register for the MOOC at any time in the [GW Online Open edX](http://openedx.seas.gwu.edu/) platform to experience the complete course (including quizzes, examples and discussion board). 
+[**"Practical Numerical Methods with Python"**](http://openedx.seas.gwu.edu/courses/GW/MAE6286/2014_fall/about) is an open, online course hosted on an independent installation of the [Open edX](http://code.edx.org) software platform for MOOCs.
+The MOOC (massive open online course) is ran by Prof. Barba at the George Washington University, while each instructor is running a local course, for credit at their institution. You can register for the MOOC at any time in the [GW Online Open edX](http://openedx.seas.gwu.edu/) platform to experience the complete course (including quizzes, examples and discussion board). 
 
 All content is open —really open, i.e., you can use, share, mod, remix— and most is available outside the course platform also (on GitHub and YouTube).
 
 ####Find the list of IPython Notebooks, with links to nbviewer, in the [Wiki](https://github.com/numerical-mooc/numerical-mooc/wiki).
 
-##List of Modules
+##List of Course Modules
 
 1. [**The phugoid model of glider flight.**](https://github.com/numerical-mooc/numerical-mooc/tree/master/lessons/01_phugoid)
 Described by a set of two nonlinear ordinary differential equations, the phugoid model motivates numerical time integration methods, and we build it up starting from one simple equation, so that the unit can include 3 or 4 lessons  on initial value problems. This includes: a) Euler's method, 2nd-order RK, and leapfrog; b) consistency, convergence testing; c) stability