Mathematical Principles of Deep Learning

Course materials

A preliminary course syllabus can be found here. Content will be added throughout fall 2022, often in the form of links to resources upon which the presentation is based. Any materials posted here are publically available for non-commercial use. In particular, they are not to be shared on for profit sites such as 'Course Hero'.

  1. Introduction

  2. Approximation by neural networks

  3. Optimization for neural networks

  4. Practical aspects

  5. Generalization

  6. Additional topics in deep learning

There are many events at TAMU in the context of deep learning, general machine learning, and data science, which may be of interest to you. Some are listed here.


Homework may be modified to fix errors, unclear formulations, or overly difficult assignments. If you find any of the above, please email me.

  1. Approximation by neural networks

  2. ReLU networks

  3. Optimization

  4. Implementation

  5. Advanced optimization

Additional (free and accessible) resources:

I am grateful for further sources or amterials. Please email me with suggestions for links to add to the list.