Artificial Intelligence and Machine Learning for Sensor Data AnalysisLaajuus (2 cr)

Objective

The student masters the basics of sensor data processing and analysis and is able to apply machine learning methods to its interpretation. They will be able to use classical and deep learning-based AI methods, such as neural networks and decision trees, to identify features of the environment and make predictions in autonomous systems.

Content

1. Processing and analysis of sensor data
2. AI methods in autonomous systems

Qualifications

AI and machine learning in sensor data analysis (2 ECTS) requires a basic understanding of sensor technology and data processing. A basic knowledge of programming and mathematical models such as statistical methods and signal processing is also recommended.

Assessment criteria, satisfactory (1)

The student
• can perform basic forms of sensor data processing and analysis.
• knows the most common machine learning methods and their basic use.

Assessment criteria, good (3)

The student
• can apply signal processing and filtering techniques to improve data quality.
• be able to implement and optimise machine learning methods for the analysis of sensor data.

Assessment criteria, excellent (5)

The student
• can develop advanced analytical methods and integrate them with real-time sensor data.
• will understand and apply neural networks and deep learning methods to support autonomous decision making.

Assessment criteria, approved/failed

AI and machine learning in sensor data analysis (2 ECTS) is passed if the student can process and analyse sensor data and apply AI methods to its interpretation. Successful completion requires the completion of exercises, simulations and reporting. Failure means that the student cannot apply signal processing or machine learning methods, cannot perform analysis of sensor data or leaves reporting incomplete.

Further information

AI and machine learning in sensor data analysis (2 ECTS) includes theory, exercises and simulations in which students process and analyse sensor data using machine learning methods. Students will use programming environments and simulation tools to apply AI to autonomous systems.