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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)
(7) Introduction to information theory (Seth Lloyd)
(8) Game Theory I - Static Games (Justin Grana)
(9) Game Theory II - Dynamic Games (Justin Grana)
(10) Introduction to Renormalization (Simon DeDeo)
(11) Fundamentals of Machine Learning (Artemy Kolchinsky)
(12) Introduction to Computation Theory (Josh Grochow)
(13) Fundamentals of NetLogo (Bill Rand)
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" !
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.
completed 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.
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.
Excellent course, fascinating covering updates to an area that I explored many years ago. Thoroughly recommended to anyone who is interested in understanding renormalisation and seeing where it is now. You don't need a very mathematical background to understand the course, though some basic background would probably help. Many helpful supplementary references and exercises. There should be a further course on his current work.
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!
Courses provided by complexity explorer are really useful for strengthening basics, scientific understanding and learning about complex systems. I am looking forward to a network science course in near future. Hopefully, it will be available in complexity explorer soon in coming time.