How to use Deep Learning for Marketing?
(offered only once)
„Deep-learning will transform every single industry.”
Andrew Ng
Abstract:
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.
Instructors:
Dr. Markus Meierer
Luca Gaegauf
Type:
Seminar
Target audience:
Master students, assigned to “Wahlpflichtbereich BWL 4”.
Frequency:
offered once
APS/ECTS-points:
3
Work load statement:
| Part | Workload | Total Time |
|
Building up theoretical foundations/ |
2*17.5h |
35h |
| Group work | 45h | 45h |
|
Online exercises |
10h | 10h |
| Total | 90h |
Maximum number of students:
25
Language:
English
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)
Grading:
2 Multiple-Choice Tests on Deep Learning Theory (40%):
- 02.03.2020
- 16.03.2020
Online Exercises (20%)
Group Project (40%)
Dates:
17.02.2020 (18:00 – 20:00 - Kick Off)
02.03.2020 (18:00 – 20:00 - Multiple-Choice-Test 1)
16.03.2020 (Multiple-Choice-Test 2 & Kick off Group Project – 18:00-20:00)
27.04.2020 (Presentation Group Project – 18:00 – 20:00)
Location: ONLILNE 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 markus.meierer@uzh.ch. 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.
Note:
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.