Advanced Machine Learning

Additional Info

  • ECTS credits: 6
  • University: University of Hamburg
  • Semester: 3
  • Topics:

     

    1. Basics: analogy; layout of neural nets, universal approximation, NP-completeness
    2. Feedforward nets: backpropagation, variants of Stochastistic Gradients
    3. Deep Learning: problems and solution strategies
    4. Deep Belief Networks: energy based models, Contrastive Divergence
    5. CNN: idea, layout, FFT and Winograds algorithms, implementation details
    6. RNN: idea, dynamical systems, training, LSTM
    7. ResNN: idea, relation to neural ODEs
    8. Standard libraries: Tensorflow, Keras, PyTorch
    9. Recent trends
  • Prerequisites:

     

    1. Skript
    2. Online-Werke:
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