Topic outline

  • The information in Oodi is outdated. The correct information is here.
    Course slack:

    • The lectures are arranged over zoom.
    • The exercise sessions are held remotely.

    Contact information
    • If you have questions regarding the course, please send an email to .

    Course description
    • Contents: Machine learning with deep neural networks. Programming using PyTorch.
    • After the course, the student understands the basic principles of deep learning: multi-layer perceptrons, convolutional and recurrent neural networks; stochastic gradient descent and backpropagation; means to prevent overfitting. The student understands methods for supervised and unsupervised deep learning. The student knows modern neural architectures used for image classification, time series modeling and natural language processing. The student has experience on training deep learning models in PyTorch.

    • Assessment: returned assignments and an exam.
    • Prerequisites:
      • NB: good knowledge of Python and numpy
      • linear algebra: vectors, matrices, eigenvalues and eigenvectors
      • basics of probability and statistics: sum rule, product rule, Bayes' rule, expectation, mean, variance, maximum likelihood, Kullback-Leibler divergence
      • basics of machine learning (recommended): supervised and unsupervised learning, overfitting
    • Course contents:
      • Introduction to deep learning
      • Optimization methods
      • Regularization methods
      • Convolutional neural networks
      • Recurrent neural networks
      • Attention-based models
      • Graph neural networks
      • Deep learning with few labeled examples
      • Deep autoencoders
      • Flow-based and autoregressive generative models
      • Generative adversarial networks