Teaching

Machine Learning

Machine Learning Basics for Information and Communication Engineering

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

Communication Engineering

Communication Systems Engineering

Topics covered in the course are the following:

• Basics of the theory of signals
  • Definition and properties of a signal. Signal transformations and elementary operations. Most common time-domain signals
  • Convolution
  • Frequency representation: Fourier series and Fourier transform
  • Complex representation: Analytic signal and Hilbert transform
• Basics of communications
  • Random processes
  • Power spectral density
  • Structure of a communication system
  • Analog and Digital Modulation
  • Introduction to Sampling and Reconstruction