Deep Learning

Reading course at Aarhus University, Department of Engineering

 
 

Study plan

The course runs in Q2 2014.
Weekly lectures are arranged by the students. Students must hand in a project report in order to pass the course.

Schedule

Week 1:

  • Neural Network Basics
  • Week 2:

  • Convolutional Neural Networks
  • Tricks for faster learning
  • Week 3:

  • Recurrent Neural Networks
  • Regularization techniques
  • Week 4:

  • Restricted Boltzmann Machines
  • Week 5:

  • Unsupervised Feature Learning
  • Autoencoders
  • Week 6:

  • Training deep network
  • Week 7:

  • Image classification
  • Course material

    Geoffrey Hinton’s online course: “Neural Networks for Machine Learning”. https://www.coursera.org/course/neuralnets

    Andrew Ng’s online tutorial on Unsupervised Feature Learning and Deep Learning. http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial

    Course participants

  • Hassan Sheta
  • Søren Østergaard Sandal
  • Anders Damkjær Hansen
  • (Mikkel Kragh Hansen – PhD student)
  • (Ander Krogh Mortensen – PhD student)
  • (Peter Christiansen – PhD student)
  • (Kim Arild Steen – Post Doc)
  • Links

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    Copyright 2014, Aarhus University Department of Engineering.
    Course responsible: Henrik Pedersen, hpe@eng.au.dk