Introduction to Deep Learning


Credit Hours:


Learning Objective:

To provide focused training on deep learning algorithms. The students should be able to acquire a principled understanding for 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.


Introduction to a principled understanding of the motivation, justification, and design considerations of the deep neural network approach to machine learning, along with hands-on projects using TensorFlow and Keras.

Topics Covered:

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


ECE 302, MA 265 (or MA 351)

Applied / Theory:

50 / 50


There will be five homework assignments.


  1. Convolutional LSTM Neural Networks
  2. Autoencoders
  3. Generative Adversarial Networks
  4. Adversarial Machine Learning
  5. Neural Networks with Memory


There will be a midterm and final exam.


Recommended: Deep Learning, 1st Edition, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press, 2016, ISBN No. 0262035618. Available at: