Neural Networks (5 ECTS)

Objective

The student
• understands the structure of different types of neural networks and the mathematical methods behind their operation
• has the skills needed to create and work with neural networks and the skills involved in programming, data processing, method selection, model construction and interpretation of results, and learns to apply these skills in a variety of machine learning tasks involving e.g. image classification and natural language processing.

Content

• Neural network as a classifier and predictor of numerical values
• Convolutional and feedback neural networks
• Neural network applications

Evaluation scale

0-5

Assessment criteria, satisfactory (1)

The student
• has achieved the objectives of the course to a satisfactory level
• is able to identify and define the concepts, models and in the subject area of the course
• has completed the learning tasks required for the course to the minimum standard.

Assessment criteria, good (3)

The student
• has achieved the objectives of the course well
• is able to identify, define and use the concepts, models and tools in the subject area of the course
• has completed the learning tasks of the course at a good level.

Assessment criteria, excellent (5)

The student
• has achieved the objectives of the course with excellent marks
• is able to identify, define and use and apply the concepts and models in the subject area of the course in a variety of ways
• has completed the learning tasks of the course at an excellent level and has put considerable own effort into their solutions.

Assessment criteria, approved/failed

The student
• has achieved the objectives of the course
• is able to identify and define the concepts, models and in the subject area of the course
• has completed the learning tasks required for the course.

Qualifications

Data Handling and Machine Learning