Teaching

Machine Learning

Machine Learning Basics for Information and Communication Engineering (Summer semester 2024)

Topics covered in the course are the following:

• Introduction to the course
  • What is machine learning for information and communication engineering (ICE), current applications
• Fundamentals of machine learning, from problem analysis/formulation to its solution and evaluation
  • Type of data, learning problems, learning techniques and evaluation methods
• Neural networks
  • Artificial neural networks, back-propagation, gradient descent, activation functions
• Introduction to deep learning for ICE
  • Relevant network architectures, e.g., CNNs, RNNs, LSTM, Transformers
• Deep learning applications to ICE
  • Learning to decode
  • Autoencoders and their application in communications
  • Generative adversarial networks (GANs) and their application in communications
  • Mutual information and channel capacity estimation