Cod: 22292
Department: DCET
Scientific area: Computer Engineering
Total working hours: 156
Total contact time: 30

This curricular unit aims to provide students with an overview of advanced techniques of computational and analytical (data) learning.

Upon completion of this course, students should understand and apply the main models of computational learning for pattern recognition.

  1. Fundamentals of deep learning.
  2. Neural networks.
  3. Fundamentals of machine learning.
  4. Convolutional neural networks (CNNs).
  5. Recurring neural networks.
  6. Encoders.
  7. Opponent networks.
  8. Generative networks.

Main Bibliography
François Chollet, Deep Learning with Python, Second Edition, 2021, ISBN 9781617296864
Supplementary Bibliography
C. Bishop, Pattern Recognition and Machine Learning, Springer 2007 1.

Aurélien Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow, O'Reilly Media, 2017


Evaluation is made on individual basis and it involves the coexistence of two modes: continuous assessment (60%) and final evaluation (40%). Further information is detailed in the Learning Agreement of the course unit.