(offered only once)
„Deep-learning will transform every single industry.”Andrew Ng
Deep Learning had a significant impact on many domains. This also applies to marketing. Consequently, this seminar has two parts. In a first part, we discuss the statistical foundations of deep learning. In a second part, you will work in a group on setting up your own deep learning model.
Dr. Markus Meierer
Master students, assigned to “Wahlpflichtbereich BWL 4”.
Work load statement:
Building up theoretical foundations/
Maximum number of students:
previous knowledge and literature:
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016
A non-technical introduction to Python (or equivalent knowledge)
A non-technical introduction to machine learning (or equivalent knowledge)
The examination of the module is carried out online. Online examination supervision is possible. The assessment consists of:
- Online exercises (DataCamp)
- Online exercises (Kaggle) & written report
Wednesdays, 16:00 - 18:00
Location: ONLINE See https://www.vorlesungen.unizh.ch
Access and Registration:
Please contact our website for enrolling and for current information. The number of participants is limited. Thus, to apply for the seminar, please send an email with a recent transcript of records to email@example.com. If you receive a positive confirmation, you are asked and allowed to officially book this seminar using the “Buchungstool”. Booking the seminar without a positive confirmation from our chair is not implying the right to attend the course. In this case the course will be grad-ed as failed.
The information in the syllabus supports the official information in the electronic university calendar (VVZ – Vorlesungsverzeichnis). In case of doubt, the official information at the VVZ is valid.