I feel extremely humbled to be able to share my experience with Deep Learning in the form of a graduate level course this semester.

The course is focusing on practical Deep Learning for engineers without leaving behind some of the theory we all love.

We plan to cover the following topics:

  • Learning Theory
  • Numerical Optimization
  • Advanced CNNs, transfer learning, and image augmentation
  • Applications to: image classification - segmentation - forensics
  • Generative Adversarial Networks
  • Open discussions on:
    • Robustness to Adversarial Attacks
    • Fairness in Machine Learning

I am working on a way to share some of the content we are creating. In the meantime, here is a GIF of me ranting about the Rosenbrock function.