Students will learn to apply deep learning techniques for projects from different industries.

Contents

• Introduction, Supervised learning, Deep neural networks, Loss functions, Training,
• Convolutional Networks, Residual Networks
• Sequence Modeling: Recurrent and Recursive Nets
• Autoencoders, Representation Learning, Transformers, Graph Neural Network
• Generative adversarial networks, Deep reinforcement learning

• Adversarial Machine Learning

• Ethics, Bias, and Fairness

Assessment

• Examination: 30%
• Coursework: 70%
  • Two lab tests ( 25%)
  • Two class tests (25%)
  • One project (20 %)

Recommended Books/Resources

• Deep Learning by Ian Goodfellow, and Yoshua Bengio, and Aaron Courville, https://www.deeplearningbook.org
• The Python Tutorial, https://docs.python.org/3/tutorial/index.html