Course unit, curriculum year 2023–2024
DATA.ML.350
Neurocomputing, 5 cr
Tampere University
- Description
- Completion options
Teaching periods
Course code
DATA.ML.350Language of instruction
EnglishAcademic years
2021–2022, 2022–2023, 2023–2024Level of study
Advanced studiesGrading scale
General scale, 0-5Persons responsible
Responsible teacher:
Martti JuholaResponsible organisation
Faculty of Information Technology and Communication Sciences 100 %
Coordinating organisation
Computing Sciences Studies 100 %
The course contains background and introductory information for the use of elementary computing elements (nodes) within layers of nodes to build feedforward neural networks to be trained in the supervised way. It also presents the method of self-organizing map based on unsupervised learning and Boltzmann machine, and gives a brief introduction to deep learning.
Learning outcomes
Prerequisites
Recommended prerequisites
Further information
Learning material
Studies that include this course
Completion option 1
Written examination and completed weekly exercises.
Completion of all options is required.
Participation in teaching
No scheduled teaching
Exam
No scheduled teaching