Learn and apply cutting-edge data analysis techniques for "big neurodata" (theory and MATLAB/Python code)
What is this course all about?
Neuroscience (brain science) is changing -- new brain-imaging technologies are allowing increasingly huge data sets, but analyzing the resulting Big Data is one of the biggest struggles in modern neuroscience (if don't believe me, ask a neuroscientist!).
The increases in the number of simultaneously recorded data channels allows new discoveries about spatiotemporal structure in the brain, but also presents new challenges for data analyses. Because data are stored in matrices, algorithms developed in linear algebra are extremely useful.
The purpose of this course is to teach you some matrix-based data analysis methods in neural time series data, with a focus on multivariate dimensionality reduction and source-separation methods. This includes covariance matrices, principal components analysis (PCA), generalized eigendecomposition (even better than PCA!), and independent components analysis (ICA). The course is mathematically rigorous but is approachable to individuals with no formal mathematics background. The course comes with MATLAB and Python code (note that the videos show the MATLAB code and the Python code is a close match).
You should take this course if you are a...
neuroscience researcher who is looking for ways to analyze your multivariate data.
student who wants to be competitive for a neuroscience PhD or postdoc position.
non-neuroscientist who is interested in learning more about the big questions in modern brain science.
independent learner who wants to advance your linear algebra knowledge.
mathematician, engineer, or physicist who is curious about applied matrix decompositions in neuroscience.
person who wants to learn more about principal components analysis (PCA) and/or independent components analysis (ICA)
intrigued by the image that starts off the Course Preview and want to know what it means! (The answers are in this course!)
Unsure if this course is right for you?
I worked hard to make this course accessible to anyone with at least minimal linear algebra and programming background. But this course is not right for everyone. Check out the preview videos and feel free to contact me if you have any questions.
I look forward to seeing you in the course!
Understand advanced linear algebra methods
Includes a 3+ hour "crash course" on linear algebra
Apply advanced linear algebra methods in MATLAB and Python
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I am a full-time educator and writer, and former professor of neuroscience. I "retired" from that position so I could focus my time and energy creating high-quality educational material just for you.I have 20 years of experience teaching programming, data analysis, signal processing, statistics, linear algebra, and experiment design. I've taught undergraduate students, PhD candidates, postdoctoral researchers, and full professors....
Great course! Once more I can say that Mike Cohen has the amazing ability to transform fairly complex topics into very clear explanations. In other words, he's great at doing dimensionality reduction on the things he teaches (if you don't get the joke yet, you will after this course :) )
Brilliantly presented, great visuals and explanations!
High quality contents, Mike puts lots of effort into the realization of his courses and you can see it (he also answers to your questions rather quickly in the Q&A section).
I strongly suggest to do this course if you already have some Linear Algebra foundations (e.g. knowledge of dot product, matrix multiplication, matrix rank, matrix inverse, eigendecomposition). If you are not confident with Linear Algebra he has another great course on Udemy (also strongly recommended) and this can be a perfect segue.
In this course he will teach you the basic mechanisms underlying some of the most relevant dimensionality reduction and source separation methods from different perspectives: from math to code.
I personally gained a lot from this course and I will definitely incorporate this acquired knowledge into my own research.
Excelente profesor, estructura muy bien sus clases, explica bien, tiene paciencia con las preguntas y las responde rápido.
Very clear to me, remembering without applying it is another thing. You must alwats take the tools out of the garden shed and sharpen them. If your job doesnt do that find one that does.
Mike Cohen has really good courses and this is one of them. He clearly explains the theory and code of topics in a manner that aids comprehension. This course is for people with experience in programming, mathematics, and have an interest in signal analysis. In conclusion, I highly recommend this course.
Overall the course is fantastic and covers a good ground on PCA, GED, IED, Linear Algebra.
He is a real teacher! The way he explain the methods and principle are great. I should just say that there is a bit redundancy in explanations, which could be less, because every one can go back and for to review the details.
But in total, I learned a lot and do appreciate his work.
excellent !!
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Adela-maria O.
I love the color coding, the duration of the videos ( the info content comes in chunks so it is easier to process).