(prereq_math)=

# Prerequisites

```{figure} /_static/img/ods_stickers.jpg
```

## Math

Knowledge of basic concepts from calculus, linear algebra, probability theory, and statistics is essential for this course. In case you need to catch up on these topics, Part I of the ["Deep Learning" book](http://www.deeplearningbook.org/) or ["Mathematics for Machine Learning"](https://mml-book.github.io/) serve as excellent resources. For a more comprehensive exploration, you can refer to relevant courses offered by [MIT courses](https://ocw.mit.edu/courses/mathematics/).

Whether a Data Scientist should be good at math or not -- that's a debatable question, I'd say a holy war. [This post](https://karpathy.medium.com/yes-you-should-understand-backprop-e2f06eab496b) by Andrej Karpathy offers a good argument supporting math as a prerequisite for doing ML (and, well, understanding what you are doing).

Check the expression below (it's from [bonus assignment 8](bonus08)). If you are not comfortable with the idea of taking its derivative w.r.t. to $w$, better refer to the resources listed above.

```{figure} /_static/img/assignment8_teaser_update_formula.png
:width: 600px
```