Course unit, curriculum year 2023–2024
DATA.ML.220
Advanced Deep Learning, 5 cr
Tampere University
- Description
- Completion options
Teaching periods
Course code
DATA.ML.220Language of instruction
EnglishAcademic years
2021–2022, 2022–2023, 2023–2024Level of study
Advanced studiesGrading scale
General scale, 0-5Persons responsible
Responsible teacher:
Konstantinos DrososResponsible teacher:
Tuomas VirtanenResponsible 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