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You are browsing the curriculum of a past academic year (2021–2022).
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Course unit, curriculum year 2021–2022
DATA.ML.220

Advanced Deep Learning, 5 cr

Tampere University
Teaching periods
Course code
DATA.ML.220
Language of instruction
English
Academic years
2021–2022, 2022–2023, 2023–2024
Level of study
Advanced studies
Grading scale
General scale, 0-5
Persons responsible
Responsible teacher:
Konstantinos Drosos
Responsible teacher:
Tuomas Virtanen
Responsible organisation
Faculty of Information Technology and Communication Sciences 100 %
Coordinating organisation
Computing Sciences Studies 100 %

The contents of the course (lectures and lab sessions) can be summarized to the following bullet points:

  • Advanced techniques for recurrent and convolutional neural networks
  • Sequence-to-sequence modelling and attention mechanisms
  • Reconstruction, denosing, and manifold learning with autoencoders
  • Generative modelling with variational autoencoders and generative adversarial neural networks
  • Adversarial training
  • Self-supervised and representation learning
  • Reinforcement learning
  • Advanced deep learning applications (e.g. domain adaptation, machine translation, natural language processing, machine vision, and machine listening)
  • Implementations in popular and open-source deep learning frameworks (e.g. PyTorch)
Learning outcomes
Compulsory prerequisites
Learning material
Studies that include this course
Completion option 1
The course will not be taught in the academic year 2021-2022
Completion of all options is required.

Exam

No scheduled teaching

Participation in teaching

No scheduled teaching