We will begin by viewing fractals as self-similar geometric objects such as trees, ferns, clouds, mountain ranges, and river basins. Fractals are scale-free, in the sense that there is not a typical length or time scale that captures their features. A tree, for example, is made up of branches, off of which are smaller branches, off of which are smaller branches, and so on. Fractals thus look similar, regardless of the scale at which they are viewed. Fractals are often characterized by their dimension. You will learn what it means to say that an object is 1.6 dimensional and how to calculate the dimension for different types of fractals.
In addition to physical objects, fractals are used to describe distributions resulting from processes that unfold in space and/or time. Earthquake severity, the frequency of words in texts, the sizes of cities, and the number of links to websites are all examples of quantities described by fractal distributions of this sort, known as power laws. Phenomena described by such distributions are said to scale or exhibit scaling, because there is a statistical relationship that is constant across scales.
We will look at power laws in some detail and will give an overview of modern statistical techniques for calculating power law exponents. We will also look more generally at fat-tailed distributions, a class of distributions of which power laws are a subset. Next we will turn our attention to learning about some of the many processes that can generate fractals. Finally, we will critically examine some recent applications of fractals and scaling in natural and social systems, including metabolic scaling and urban scaling. These are, arguably, among the most successful and surprising areas of application of fractals and scaling. They are also areas of current scientific activity and debate.
This course is intended for anyone who is interested in an overview of how ideas from fractals and scaling are used to study complex systems. The course will make use of basic algebra, but potentially difficult topics will be reviewed, and help is available in the course discussion form. There will be optional units for more mathematically advanced students and pointers to additional resources for those who want to dig deeper.
1. Introduction to fractals. Self-similarity dimension. Review of logarithams and exponents.
2. Box-counting dimension. Further examples of fractals. Stochastic fractals.
3. Power laws and their relation to fractals. Rank-frequency plots. How to estimate power law exponents.
4. Empirical examples of power laws. Other long-tailed distributions: log normals and stretched exponentials. Implications of long tails.
5. Mechanism for generating power laws. Rich-get-richer phenomena. Phase transitions. Other mechanisms.
The course was very well taught by the instructor, which provided a nice overview on the subject. However, I still missed some "real -life examples" during the course, yet the instructor discussed two nice applications, i.e., metabolic and urban scaling, which I have never heard before. Maybe further examples, like stock market or large-scale structure of the Universe, would improve the course immersion even more. But taking into account the limited duration (2 months?) that such mooc should take, the content is very good and broad. Considering also that many of the attendants are not trained …
The course was very well taught by the instructor, which provided a nice overview on the subject. However, I still missed some "real -life examples" during the course, yet the instructor discussed two nice applications, i.e., metabolic and urban scaling, which I have never heard before. Maybe further examples, like stock market or large-scale structure of the Universe, would improve the course immersion even more. But taking into account the limited duration (2 months?) that such mooc should take, the content is very good and broad. Considering also that many of the attendants are not trained in the STEM field (as in my case), the introduction regarding logarithms, power laws etc. were very didactic and clarifying. Another positive point goes to the interviews, which were very insightful.
I am giving therefore a 4 out of 5, since I still believe that some points (the real life examples) could be improved a little bit, but these are just minor improvements that do not compromise the overall's quality of the course at all. So, I am very thankful for Prof. Feldman for providing such nice course.
If you are interested in or are pursuing computer science or digital visual arts, I highly recommend taking this course. This course totally beats that one-day lesson on fractals in algebra I had in middle school. Even though the professor doesn't assume much more of a background in math than high school algebra, he takes you step by step through logarithms and power laws if you need a refresher. Even though I didn't complete the course, the first few units on fractals were not only very interesting, but I found them very complementary to my study of recursion in computer science. If you're le…
If you are interested in or are pursuing computer science or digital visual arts, I highly recommend taking this course. This course totally beats that one-day lesson on fractals in algebra I had in middle school. Even though the professor doesn't assume much more of a background in math than high school algebra, he takes you step by step through logarithms and power laws if you need a refresher. Even though I didn't complete the course, the first few units on fractals were not only very interesting, but I found them very complementary to my study of recursion in computer science. If you're learning to program in Java, I would recommend downloading the Processing language IDE and trying to program the fractals that the professor introduces in the course, or if you're more into graphics software, downloading Inkscape and trying out the L-systems method of producing fractals on that. This is a very gentle but interesting and applications-packed course if this is your first course in complexity.
I really enjoyed this course. The material was interesting and the explanations were clear. The instructor was engaging. So I feel like an ingrate complaining, especially about something that is not within hos control. But I hope you'll find a way to adjust for a presenter's left handedness. Watching the back of his hand as he wrote on the whiteboard got to be frustrating after a while.
Thank you so much for offering ordinary people the opportunity to learn such cool stuff!
Nice course but this instructor is basically following his textbook written half a decade ago, from old papers, nothing much new to be honest. Yet a good course though, but I think instructors should risk more and treat students with more respect, we could just read his textbook.
This was a great class as in introduction to fractals and scaling. The metabolic scaling section was fascinating, and has encouraged me to do more in-depth reading and research on the subject. I'm definitely looking forward to the next Complexity Explorer course!
Good course, really peaked my interest in the topics presented, good introduction! Could use some more hands on computational exercises to connect better with the material but was otherwise a very good course.
completed this course, spending 3 hours a week on it and found the course difficulty to be easy.
Very nice course! Quite easy without going deep into math behind, without any computational assignments. But topic is rather interesting and explanations were very clear and with good examples. You can watch it on 1.5x speed easily.
For lack of my own personal time I was not able to finish the course but the course itself has ample time to be finished. The homework is great, the easy questions make sure you get the basics down and the harder questions are challenging and within the range of what is being taught.
There are many references to real world practical usage, books, articles and software. The community of students and teacher are lively and supportive; they help out with doubts and always bring something new to contribute to the learning process in discussions.
I think that anyone who does this course may come out of it learning enough to understand what fractals are and have enough baggage to enter the more detailed and (more) mathematically challenging material on their own; or at least know exactly what is needed to advance.
I wasn't sure I'd have the time to take this course but I had taken another one taught by Dave Feldman last summer (Dynamical Systems and Chaos) and it was great. I had free time then but, having just started a job, I was more concerned about my ability to devote the necessary time to the course. I ended up giving it a go and settled on a routine of downloading the lectures and watching them during my time at the gym, which not only allowed me to focus on the lectures but made me look forward to going to the gym! I work in human computer interaction and I'm hoping to direct my research in this direction but, even if it's a tough fit and I can't find a way to integrate things, the course is incredibly rewarding in and of itself and offers tasty brain food to chew on.
completed this course, spending 4 hours a week on it and found the course difficulty to be medium.
As a social scientist who wanted to learn more about fractals, this class was perfect. Dave makes the mathematics very accessible. He speaks in plain English and has a charming way of breaking complex ideas down into simple building blocks. There are tutorials and optional exercises to help you learn things you might be rusty on. The homework and practice exams prepare you very well for the quizzes. The videos are mostly between 5-10 minutes so you can take the course in bite-size pieces as your time allows. Dave also provides resources for each module to continue your learning after the course. Highly recommended!
completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
I loved the course! for me linking something statistical to fractals was a revelation. I see myself applying some of what I've learnt in my work. This series of lectures are applied in nature rather than purely theoretical. The level is fairly easy for a non mathematician. Modeling any data before feeding it to a statistical blackbox like an SVM or a neural network is an imperative but almost overlooked aspect of data science. This course is necessary in that modelling toolbox.
Professor Feldman is terrific at making the complex understandable. He's organized and the course materials and supplementary resources are excellent. He handles the occasional snafu with aplomb and humor. My favorite was the time there was a loud explosion followed by a power outage during recording. He carried on with the lesson seamlessly all the while hoping his computer wasn't " fried."
Thank you, Professor Feldman for a great learning experience.
This is an amazing course for anyone that wants to learn about this topic and does not have the time to go through all the mathematical rigour behind it. The lecturer has a natural skill to explain in a very simple way things that may seem very complicated in some textbooks or courses. You will have fun following these lectures and learn while you can hold a cup of coffee in one hand and a pocket calculator in the other to compute dimensions of fractional objects.
This is another fantastic course from Prof. Feldman (the other one being Introduction to Dynamical Systems and Chaos), offered by the Santa Fe Institute. Prof. Feldman is able to explain difficult topics in a simple way, with plenty of practical examples taken from the real world and a bit of humour. Warmly recommended if you are interested in Scaling, even if you don't have a lot of mathematical background.
Great overview of the topic with excellent resources and interviews for additional directions for more the interested students. A bit light on the math, but I understand the intent on making it accessible to the largest audience. Everyone should take this course since it is a fundamental topic to the future of systems science, which pretty much encompasses everything these days
Great overview of fractals, followed by a thorough in-depth exploration of the math and concepts behind them, that of self-similarity and Power Laws. Some practical implications peppered throughout but particularly near the end of the course. I admire David's restraint in implicating too much meaning behind self-similarity, while underscoring the potential power endowed by it.
Another excellent course by Complexity Explorer. A great introduction to fractals and self-similarity, concepts with very important real-life applications across many disciplines. Dave Feldman is an amazing instructor, always making sure that the concepts and ideas discussed are presented in a clear and practical way. Would definitely recommened to everyone interested
I've done another course with David Feldman and found his style to be really engaging, he unpacks the concepts really simply which tells me he has a deep understanding of these ideas and notions. I actually took this course because he was doing the lecturing. I have been involved with complexity concept for some decades and still learn new things from David.
The course keeps its promises. It's an introduction to the field, that shows you many different aspects, from theory to applications. It gives you an intuition of the things, without going in the technical details, but providing you, each time, the needed references, if you want to go deeply in the questions. Well done, as usual at ComplexityExplorer, thanks!