This course covers several mathematical techniques that are frequently used in complex systems science. The techniques are covered in independent units, taught by different instructors. Each unit has its own prerequisites. Note that this course is meant to introduce students to various important techniques and to provide illustrations of their application in complex systems. A given unit is not meant to offer complete coverage of its topic or substitute for an entire course on that topic.
The units included during this offering of the course are:
(1) Introduction to differential equations (David Feldman)
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
This course begin at a point in the matter which don't know it really are. It's like attending in a class in final stage! Too much math without description. Poor and very summarized definition at the beginning then falling into math without knowing why.
I was looking for diffusion and random walks to use in social science and i have strong mathematics background but nothing learned about the subject.
It's just solving some math work without explanation of what is and why is, so i quit.
I suggest to put in prerequisites, being "Undergraduates of fluid physics" !
It is a clear and easy to understand introductory course on machine learning for non-experts. The bite-size lecture videos and quiz questions after each video made it easier to understand the concept step by step.
Excellent and surprising connections. Some references to rigorous treatment and more example problems will be appreciated. Often it is mentioned about non-renormalization of gravity but no further info is provided.
An excellent tutorial! If you are looking for a quick way to pick up basic working knowledge of information theory you do not have to look any further as this course will get you up and running in a few hours. Moreover, it even does not assume too much of a mathematical knowledge. As long as one is well versed with high school algebra (especially logarithms and basic probability theory) everything discussed in this tutorial should be easy to process and understood.
Outstanding introductory tutorial that is supplementing my Stochastic Processes class extremely well. This also clarified classes I took where we simulated Quantum Montecarlo Methods. It provided me a map to work off of for certain understandings I needed. I am working on the supplementary material too. This is a great starting point for self study!
Subhashaudited this course, spending 1 hours a week on it and found the course difficulty to be very easy.
It was a great tutorial! I keep using the Matlab's ode45 solver but didn't know how it works, this ODE tutorial answered it and that too in a light and illustrative fashion. Using the same example throughout has also helped in easy understanding.
This review pertains to the tutorial by Elizabeth Bradley.