This module aims at providing insights into the most foundational architectures of deep learning algorithms within both supervised and unsupervised learning, thus building a strong basis for further exploration of more specific and cutting-edge techniques. Real-world problems that are approached with the techniques covered in this module include the development of advanced recommender systems (marketing), computer vision models (healthcare, economics), powerful unsupervised pattern recognition systems (fraud detection or credit default prediction in finance) and sequence modelling for (attempts of) stock market index prediction.
Upon completion, students will acquire theoretical and practical knowledge, enabling them to understand and apply central techniques and concepts of deep learning approaches as well as the fundamentals of artificial intelligence for analytics. They will be able to select and apply appropriate methods to real-world problems and critically reflect on them.
The objective is that the student after the module possesses the necessary knowledge on:
The objective is that the student after the module possesses the necessary skills in:
The objective is that the student after the module possesses the necessary competences in:
For information see § 17.
|Name of exam||Applied Deep Learning and Artificial Intelligence|
|Type of exam|
Oral examGroup examination with max. 4 students.
|Assessment||7-point grading scale|
|Type of grading||Internal examination|
|Criteria of assessment||The criteria of assessment are stated in the Examination Policies and Procedures|
|Danish title||Anvendt Deep Learning og kunstig intelligens|
|Semester||Spring and Autumn
MSc in Economics and Business Administration (Business Data Science): The module is placed in the Spring.
MSc in Economic: The module is placed in the Autum.
|Language of instruction||English|
|Location of the lecture||Campus Aalborg|
|Responsible for the module|
|Study Board||Study Board of Economics and Business Administration|
|Department||AAU Business School|
|Faculty||Faculty of Social Sciences and Humanities|