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Deep learning

Individual course

Max amount of FITech students: 75

Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course.

The course introduces the fundamental and current topics of deep learning.

In every weekly assignment, the students get to train a deep neural network for various tasks including image classification, machine translation, solving reasoning problems, few-shot learning and generative modeling. The course covers the most recent advances (such as unsupervised and self-supervised deep leaning) to give the student a good starting position to do research in this field.

After the course, the student

  • understands the general principles of training deep neural networks (backpropagation, stochastic gradient descent, regularization)
  • knows the most common neural network architectures (convolutional and recurrent neural networks, graph neural networks and transformers)
  • has practical experience in implementing these models from scratch in PyTorch.

Course material

Lecture slides and lecture notes, research papers, online tutorials on PyTorch.

Teaching methods

The lectures are organised in class (voluntary). The material including the lectures will be available online. The exercises are organised via Zoom.

More information in the Aalto University study guide.

You can get a digital badge after completing this course.

koneoppiminen, tekoäly, syväoppiminen, algoritmit, ohjelmointi

Further information about the studies

Aalto University
Kirsi Viitaharju

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
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ICT Studies
AI and machine learning
Course code:
Study credits:
0 €
Course level:
Teaching period:
Application deadline:
Host university:
Aalto University
Who can apply:
Adult learner,
Degree student
Teaching method:
Teaching language:
General prerequisites:
Good knowledge of Python and numpy (important!), basics of linear algebra (vectors, matrices, eigenvalues and eigenvectors) and basics of probability and statistics (sum rule, product rule, Bayes' rule, expectation, mean, variance, maximum likelihood, Kullback-Leibler divergence)
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