Aim: 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 foundation 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 (attempts of) stock market index prediction.
This module focuses on the most recent developments in the field of data science that build on deep learning and different architectures of artificial neural networks. While conceptually, these techniques were already conceived in the 70s and 80s, it was only recently that Big Data created a need and modern computers allowed to use them in practice. Today, deep learning algorithms are behind a variety of online and offline applications. They are enabling massive recommender systems in online retail and entertainment and powering artificial intelligence applications in medical diagnostics. Vast interest and investment in R&D within this area spurred progress of these techniques and made them more accessible. Only a few years ago deep learning and AI were barely known outside computer science departments. Today, these approaches are widely used in medicine, natural sciences and increasingly seen in social science as well as humanities.
While many of these techniques constitute compelling approaches, especially for predictive modelling, yet they do not make more traditional modelling approaches (e.g. techniques learned in M1) obsolete, but offers many synergies. Therefore, the module is structured in a way that makes it easy for students to see, where the analysis can make use of deep learning approaches as an alternative to more established techniques (e.g. regression analysis). Emphasis will be put on outlining the cases in which traditional (often leaner) methods are more suited.
Upon completion, students will acquire theoretical and practical knowledge, enabling them to understand and explain 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.
Lectures will be complemented by online resources and e-learning tools such as podcasting, online tutorials, and mini-assignments, as integral parts of the teaching methodology in order to enhance student engagement outside the classroom. Physical face-to-face time will be centred around the tacit and interactive components of the problem-solving processes.
|Prøvens navn||Deep Learning and Artificial Intelligence for Analytics|
Skriftlig og mundtligPortfolio exam:
60% obtained through various graded (and supervised peer-graded) problem sheets and mini-assignments throughout the module.
40% final internal evaluation seminar with oral presentation, peer-evaluation (opponent group), internal critique and discussion departing from the assignment and presentation.
|Vurderingskriterier||Vurderingskriterierne er angivet i Universitetets eksamensordning|
|Engelsk titel||M3: Deep Learning and Artificial Intelligence for Analytics|
|Studienævn||Studienævn for Økonomi|
|Institut||Institut for Økonomi og Ledelse|
|Fakultet||Det Samfundsvidenskabelige Fakultet|