Understand the Fourier transform and its applications
- Learn about one of the single most important equations in all of modern technology and therefore human civilization.
The fundamental concepts underlying the Fourier transform
Sine waves, complex numbers, dot products, sampling theorem, aliasing, and more!
- Interpret the results of the Fourier transform
- Apply the Fourier transform in MATLAB and Python!
- Use the fast Fourier transform in signal processing applications
- Improve your MATLAB and/or Python programming skills
- Know the limitations of interpreting the Fourier transform.
- A curious mind!
- Some MATLAB or Python experience is useful but not required
- High-school math (calculus is not necessary)
- Previous knowledge of the Fourier transform is NOT necessary!
- * Manually correct English captions *
The Fourier transform is one of the most important operations in modern technology, and therefore in modern human civilization. But how does it work, and why does it work?
What you will learn in this course:
You will learn the theoretical and computational bases of the Fourier transform, with a strong focus on how the Fourier transform is used in modern applications in signal processing, data analysis, and image filtering. The course covers not only the basics, but also advanced topics including effects of non-stationarities, spectral resolution, normalization, filtering. All videos come with MATLAB and Python code for you to learn from and adapt!
This course is focused on implementations of the Fourier transform on computers, and applications in digital signal processing (1D) and image processing (2D). I don’t go into detail about setting up and solving integration problems to obtain analytical solutions. Thus, this course is more on the computer science/data science/engineering side of things, rather than on the pure mathematics/differential equations/infinite series side.
This course is for you if you are an aspiring or established:
- Data scientist
- Computer scientist (MATLAB and/or Python)
- Signal processing or image processing expert
- Curious independent learner!
What you get in this course:
- >4 hours of video lectures that include explanations, pictures, and diagrams
- pdf readers with important notes and explanations
- Many exercises and their solutions! (Note: exercises are in the pdf readers)
- MATLAB code, Python code, and sample datasets for applications
With >3000 lines of MATLAB and Python code, this course is also a great way to improve your programming skills, particularly in the context of signal processing and image processing.
Why I am qualified to teach this course:
I have been using the Fourier transform extensively in my research and teaching (primarily in MATLAB) for nearly two decades. I have written several textbooks about data analysis, programming, and statistics, that rely extensively on the Fourier transform. Most importantly: I have taught the Fourier transform to bachelor’s students, PhD students, professors, and professionals, and I have taught to people from many backgrounds, including biology, psychology, physics, mathematics, and engineering.
So what are you waiting for??
Watch the course introductory video to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up.
I hope to see you soon in the course!
- Students who need to know the Fourier transform for courses.
- Scientists who need to know the Fourier transform for research.
- Data scientists who need to do spectral analysis.
- Someone doing digital signal processing or image processing (filtering, signal separation, etc.)
- Someone who learned the FT by solving integral equations but wants more insight into what it means.
- Programmers looking for tips about optimizing code that involves FFT.
- Someone who is curious what the Fourier transform is and why it’s so important.
- Someone who uses the FFT but wants a better understanding of what it means, why it works, and how to interpret the results.