Deep Learning / Machine Learning



Aly El Gamal, Asst. Professor Electrical & Computer Engineering

Course Outcome

To provide focused training on deep learning algorithms. The students should be able to acquire a principled understanding of the various techniques that have a proven successful record in solving important engineering problems. Further, hands-on experimental training will be provided through the course projects.


  • Undergraduate level knowledge of linear algebra and probability (ECE302 and MA265)
    • Note: Purdue has short courses in these topics available as refreshers

Students will learn:

  1. How to make design choices regarding the construction of deep learning algorithms.
  2. The history and justification for state-of-the-art deep learning algorithms.
  3. How to implement, optimize and tune state-of-the-art deep neural network architectures.
  4. The security aspects of state-of-the-art deep learning algorithms.
  5. Open research problems in deep learning and proposed approaches in the literature to tackle them.

Topics include:

  • Deep Feedforward Networks
  • Regularization for Deep Learning
  • Optimization for Training Deep Models
  • Convolutional Neural Networks
  • Recurrent and Recursive Neural Networks

CEUs: 1.5

For further information and to register, please email