# Welcome to Statistical Machine Learning Course

**Mahmood Amintoosi, Spring 2026**

Computer Science Dept, Ferdowsi University of Mashhad

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A short podcast (about 5 minutes) has been prepared with **NotebookLM**, introducing the content of the course.  
You can listen to it here (In Persian):

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  <source src="Welcome to Statistical Machine Learning Course - Persian.wav" type="audio/wav">
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# Table of Contents

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# Course Resources for Statistical Machine Learning

- [Pattern Classification](http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471056693.html), by Richard O. Duda, Peter E. Hart, David G. Stork {cite}`Duda2000`

- [The Elements of Statistical Learning: Data Mining, Inference, and Prediction ](https://hastie.su.domains/ElemStatLearn/), by Trevor Hastie, Robert Tibshirani, and Jerome Friedman {cite}`ESLII_print12`

- [Pattern Recognition and Machine Learning](https://www.rasa-ai.com/pattern-recognition-and-machine-learning-bishop/),  by Christopher M. Bishop {cite}`bishop2016pattern`

- [Machine Learning: A Bayesian and Optimization Perspective](https://www.google.com/books/edition/Machine_Learning/l-nEDwAAQBAJ?hl=en), by Sergios Theodoridis {cite}`theodoridis2020machine`

- [Python Data Science Handbook by Jake VanderPlas](https://jakevdp.github.io/PythonDataScienceHandbook/): This website contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks.

<!-- - [FUM Drive for DM course](https://fumdrive.um.ac.ir/index.php/s/pkD6FsZ5nyJiHY7) -->

<!-- ## Further Machine Learning Resources

- [The Scikit-Learn website](http://scikit-learn.org): The Scikit-Learn website has an impressive breadth of documentation and examples covering some of the models discussed here, and much, much more. If you want a brief survey of the most important and often-used machine learning algorithms, this is a good place to start.

- [_Introduction to Machine Learning with Python_](http://shop.oreilly.com/product/0636920030515.do), by Andreas C. Müller and Sarah Guido (O'Reilly). This book covers many of the machine learning fundamentals discussed in these chapters, but is particularly relevant for its coverage of more advanced features of Scikit-Learn, including additional estimators, model validation approaches, and pipelining.

- [_Machine Learning with PyTorch and Scikit-Learn_](https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312), by Sebastian Raschka (Packt). Sebastian Raschka's most recent book starts with some of the fundamental topics covered in these chapters, but goes deeper and shows how those concepts apply to more sophisticated and computationally intensive deep learning and reinforcement learning models using the well-known [PyTorch library](https://pytorch.org/). -->

# Course Staff

## Instructors

<table style="width:100%;">  
  <tr>  
    <td style="width:20%;">  
      <img src="_images/M-Amintoosi.jpg" alt="Mahmood Amintoosi" style="width:100px;height:auto;">  
    </td>  
    <td>  
      <strong><a href="https://mamintoosi.github.io/">Mahmood Amintoosi</a></strong><br>  
      Email: m.amintoosi AT um.ac.ir
    </td>  
  </tr>  
</table>

---

## Teaching Assistants

<table style="width:50%;">
  <tr>
    <td style="width:120px; text-align:center; vertical-align:middle;">
      <img src="_images/H-Mehr.jpg" alt="Hoda MehrBagherpour" style="width:100px;height:auto;">  
      <br>
      <strong><a href="https://github.com/HodaMehr2000">Hoda MehrBagherpour</a></strong><br>  
      Email: mehrbagherpour AT mail.um.ac.ir  
    </td>
    <td style="width:120px; text-align:center; vertical-align:middle;">
      (TA Head)
    </td>
  </tr>
  <!-- <tr>
  <td> <br> </td>
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  <tr>
      <td style="width:120px; text-align:center; vertical-align:middle;">
      <img src="_images/Mr-Dashtban.jpg" alt="Mr. Dashtban" style="width:100px;height:auto;"> <br>
      Mr. Dashtban
    </td>
    <td style="width:120px; text-align:center; vertical-align:middle;">
      <img src="_images/Miss-Kiani.jpg" alt="Miss. Kiani" style="width:100px;height:auto;"> <br>
      Miss. Kiani
    </td>
  </tr> -->
</table>

## Questions?

I will be having office hours for this course on Saturday (10:00 AM--11:30 AM). If this is not convenient, email me at m dot amintoosi AT um.ac.ir, talk to me after class or [schedule an appointment via Calendly](https://calendly.com/m-amintoosi/30min). Also you can find me at [Bale](https://web.bale.ai/chat), [Rubika](https://web.rubika.ir/), [Eitaa](https://web.eitaa.com/) and [FUM-VU](https://vu.um.ac.ir/).


---

```{note}
These lectures were built using the new Sphinx-based [Jupyter Book
2.0](https://jupyterbook.org/) tool set, as part of the
[ExecutableBookProject](https://ebp.jupyterbook.org/en/latest/).  They are
intended mainly as a demonstration of these tools.
Instructions for how to build them from source can be found in the Jupyter
Book documentation.
```

## References

```{bibliography}
```