LEARNING OUTCOMES
After completing the course, the student should be able to
- explain and use basic methods of statistical machine learning and deep learning
- implement statistical machine learning algorithms or deep learning models,
- apply them in physical systems, such as in wireless communication systems.
Credits: 5
Schedule: 13.09.2024 - 22.11.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Esa Ollila
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
optimal estimation and detection, Cramer-Rao lower bound, compressed sensing, sparse signal recovery, fundamentals of wireless channels, deep learning models, calibration of machine learning models, uncertainty quantification. Implementing algorithms on a computer are a part of the course and the programming language is Python.
Assessment Methods and Criteria
valid for whole curriculum period:
Exercises, homework assignments, project work. See course page in Mycourses.
Workload
valid for whole curriculum period:
- Lectures
- Tutorials
- exercises
- Self-study
- project work
DETAILS
Study Material
valid for whole curriculum period:
Course literature, lecture notes as well as lecture slides. See course page in Mycourses.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Autumn I - II
2025-2026 Autumn I - IIRegistration:
-