(book:statistics)=
# Statistics - basics

This material is part of the [**basics-books project**](https://basics2022.github.io/bbooks). It is also available as a [.pdf document](_build/latex/book.pdf). 

**Contents.**
```{dropdown} [Introduction to statistics](intro:intro)
:open:

Different approaches to statistics and **descriptive statistics**

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```{dropdown} [Probability theory](prob:intro)
:open:
```

```{dropdown} [Inferential statistics](infer:intro)
:open:

Inferential and Bayesian statistics
```

```{dropdown} [Introduction to Machine Learning: SL, UL, ML](ml:intro)
:open:

Machine learning (ML) is a branch of artificial intelligence (AI) focused on designing systems that can learn from data to improve their performance on a task. ML frameworks include supervised learning (e.g., regression and classification), unsupervised learning (e.g., clustering, compression, principal component analysis), and reinforcement learning (e.g., planning and control). ML emphasizes practical problem-solving, grounded in statistical methods, numerical optimization, and enabled by advances in computing hardware.
```

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It is also available as a [.pdf document](_build/latex/book.pdf).
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