Deep learning (5 cr)

Objective

You can implement deep learning models through programming.
You know how to use libraries designed for deep learning.
You understand the significance of the cost function and gradients in optimization.
You can communicate using terminology related to neural networks.
You understand the purpose of activation functions.
You know how to modify your learning algorithm or data to improve model performance.

Content

What libraries are used for programming deep learning models?
How are deep learning models programmed?
How can the performance of a learning algorithm be improved?
What concepts are related to deep learning?
What are the application areas of deep learning?

Evaluation

a. use professional vocabulary and concepts in an expert way in different situations.
b. assess information sources critically.
c. work as team members in working life expert duties and identify and describe the problems of the professional field.
e. choose appropriate models, methods, software and techniques according to the purpose and justify these choices.
g. apply critically the ethical principles of the professional field in different situations.

Course material

Lecture notes and calculations.

Study forms and methods

Final exam.

RDI and work-related cooperation

This course does not include RDI and work-related cooperation.

Evaluation scale

1-5

Qualifications

Knowledge of applied mathematics, data analysis, and machine learning is required.