Deep Learning track

Here we provide a list of topics covered by the Deep Learning track, split into methods and computational aspects. The ordering of topics does not reflect the order in which they will be introduced.

As explained in the overview of courses, the track consists of four levels. If you would like to understand how the curriculum is reflected by each of the four levels, please contact us, and we'll be happy to explain.



Regression problems

Classification problems

Performance evaluation, result significance, and common mistakes

  • Performance metrics and their relationship to the problem’s practical objective
  • Dealing with imbalanced datasets
  • Choosing the right baselines
  • Ablation studies
  • Significance of results

  • Supervised machine learning: basics

    Optimization methods

    Deep Learning libraries

  • Computation graphs
  • Graph-based computations vs. eager execution
  • TensorFlow and Keras
  • PyTorch
  • Production considerations
  • Research considerations

  • Practical aspects of training neural networks

    Practical aspects of training machine learning models

    Improving the performance of machine-learning models

    Neural networks based on fully connected layers

    Convolutional neural networks (CNN)

    Recurrent neural networks (RNN)

    Neural networks with entity embedding layers

    Natural Language Processing (NLP)

    Unsupervised machine learning: basics


    Generative Adversarial Networks (GAN)

    Vulnerabilities of machine-learning systems

    Time-series analysis

    Causal inference

    Reinforcement learning


    Computational aspects

    Command line interfaces and operating systems

    GPU computing

    Optional: Building one's own GPU-powered computer

    Python: language structure

    Python: libraries

    Python: computation speed

    Data formats


    Cloud computing