Artificial Intelligence and Deep Learning

This course aims at providing students with a basic understanding on artificial intelligence and deep learning technology. The topics to be covered are artificial neural networks, backpropagation, deep auto-encoder, convolutional Neural Network (CNN), recurrent Neural Network (RNN), strategies for training deep architectures, handling overfitting, cross-validation, meta-heuristic searching for parameter tuning. This is followed by hands-on implementation of deep/machine learning algorithms using Python, with applications ranging from image classification, recognition and generation.

After finishing the course, students will be able to

• Master the basic concept of artificial intelligence and deep learning.
• Master the Python programing language for implementing deep/machine learning models.
• Apply deep/machine learning in novel applications.
  

Pre-requisite: ELEC3241 Signals and linear systems
Assessment: 45% practical work, 55% continuous assessment