# Neurocomputing

# Overview

This website contains the materials for the module **Neurocomputing** taught by Dr. Julien Vitay at the Technische Universität Chemnitz, Faculty of Computer Science, Professorship for Artificial Intelligence.

Each section/lecture is accompanied by a set of videos, the slides and a written version of the content. The (slightly outdated) videos are integrated in the lecture notes, but you can also access the complete playlist on Youtube.

Exercises are provided in the form of Jupyter notebooks, allowing to implement in Python at your own pace the algorithms seen in the lectures and to learn to use machine learning libraries such as `scikit-learn`

, `keras`

and `tensorflow`

. A notebook to work on (locally or on Colab) and the solution are available in the Exercises section.

## Syllabus

### Lectures

**Introduction****Linear algorithms****Neural networks****Computer Vision**

**Generative modeling****Recurrent neural networks****Self-supervised learning****Outlook**

### Exercises

Notebooks and videos are in the List of Exercises. Below are links to the rendered solutions.

## Recommended readings

(Murphy, 2022) Kevin Murphy. Probabilistic Machine Learning: An introduction. MIT Press, 2022. https://probml.github.io/pml-book/book1.html

(Goodfellow et al., 2016) Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.

(Chollet, 2017) François Chollet. Deep Learning with Python. Manning publications, 2017. https://www.manning.com/books/deep-learning-with-python.

(Haykin, 2009) Simon S. Haykin. Neural Networks and Learning Machines, 3rd Edition. Pearson, 2009. http://dai.fmph.uniba.sk/courses/NN/haykin.neural-networks.3ed.2009.pdf.