*Here, you find my whole video series about Fourier Transform in the correct order and I also help you with some text around the videos. If you want to test your knowledge, please use the quizzes, and consult the PDF version of the video if needed. In the case you have any questions about the topic, you can contact me or use the community discussion in Mattermost and ask anything. Now, without further ado, let’s start:*

#### Part 1 - Introduction

Let’s start by explaining what we will cover in this course. We will start with a discussion about **Fourier series** for periodic functions. This is an important topic to understand while the Fourier transform as an integral transform actually makes sense. After this, we can define the **continuous Fourier transform** and discuss the properties. Especially for the first part, we will need a lot of Linear Algebra topics, especially about inner products and orthonormal bases.

#### Part 2 - Trigonometric Polynomials

Let’s start with the discussion of **Fourier series**. For this, we first have to define what a so-called **trigonometric polynomial** is. We will describe the real version of these and the complex version. Both are essentially equivalent but for the introduction into the topic, the representation with sine and cosine functions is easier to visualize.

#### Part 3 - Orthogonal Basis

We consider the vector space $ \mathcal{P}_{2 \pi -per} $ of the real trigonometric polynomials together with an inner product. This means that the notion **orthogonality** makes sense. In particular, the sine and cosine functions are perpendicular. It turns out that we find an orthogonal basis for this vector space.

#### Part 4 - Orthonormalbasis of Trigonometric Functions

After finding an orthogonal basis in the last video, we can ask the question if we also have a nice orthonormal basis, where every vector is normalized with respect to the given inner product. This is possible if we scale the standard cosine and sine functions with a suitable constant. Alternatively, it’s also possible to scale the inner product to get an orthonormal basis. Here, we consider three possible cases for such an inner product.

#### Part 5 - Integrable Functions

As a quick interlude, we have to talk about the vector spaces $ \mathcal{L}^1 $ and $ L^1 $ and the corresponding ones denoted by $ \mathcal{L}^2 $ and $ L^2 $. Rougly speaking, they just describe the vector spaces given by the **integrable functions** and the **square-integrable functions**, respectively. To describe them in an efficient way, we have to put in some knowledge from measure theory.

#### Part 6 - Fourier Series in L²

Now, we are finally ready to define the **Fourier series** for periodic functions that are integrable over one period. However, we first focus on square-integrable functions because there we have an inner product and the interpretation given by an orthogonal projection. The Fourier series will be denoted by $ n\mapsto \mathcal{F}_n(f) $.

#### Part 7 - Complex Fourier Series

We already know that trigonometric polynomials can alternatively be represented by complex exponential functions of the form $ e^{i k x} $. However, we didn’t talk how the translation from the cosine and sine functions actually works. Let’s discuss this now and let’s also rewrite the Fourier series in this form. It turns out that everything looks much simple with complex exponential functions.

#### Summary of the course Fourier Transform

- You can download the whole PDF here and the whole dark PDF.
- You can download the whole printable PDF here.