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Assistant Professorship for Intelligent Information Systems
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  1. Friedrich-Alexander-Universität
  2. School of Business, Economics and Society

Assistant Professorship for Intelligent Information Systems

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Natural Language Processing for Business Analytics

In page navigation: Teaching
  • Bachelor
    • Business Analytics: Technologien, Methoden und Konzepte
  • Master
    • Development of Deep Vision Systems
    • Natural Language Processing for Business Analytics
    • Business Analytics: Case Studies (Winter Term)
    • Business Analytics: Case Studies (Summer Term)
    • Business Analytics: Research Seminar (Winter Term)
    • Business Analytics: Research Seminar (Summer Term)
  • Theses

Natural Language Processing for Business Analytics

Natural Language Processing for Business Analytics

Over the last few years, natural language processing (NLP) has been one of the most revolutionary fields of artificial intelligence (AI). NLP gives machines the ability to extract meaning from human languages and make decisions based on this data. In other words, NLP helps computers communicate with humans in their own language.

This course provides the necessary fundamentals for the development of modern NLP systems based on machine learning. We cover a wide range of feature extraction and modeling techniques including recent innovations in the field of deep neural networks and their capabilities of automated feature learning. Additionally, we also look at further aspects such as ethical issues and the use of explainable artificial intelligence methods to gain insights about the functioning of learned models.

The course has a strong practical focus. At the beginning of the semester, all fundamentals are provided and students with less knowledge in programming have the opportunity to catch up in a bootcamp introductory session before learning the fundamentals in hands-on exercises. Afterwards, students are encouraged to work (in groups) on real projects to apply the methods and concepts learned during the teaching sessions. The results are presented and discussed at the end of the semester.

Please note that the number of participants for this course is limited. Below you can find further information on the registration process. After a successful course registration, more details on course updates, course materials, technical setup, and other information will be announced in StudOn.

Learning objectives and skills

The students
  • understand the challenges for developing NLP-based systems,
  • understand the basic techniques that have paved the way for nowadays performance of language processing systems,
  • explain the general pipeline of NLP based on deep neural networks,
  • compare and evaluate different system configurations,
  • discuss ethical issues that have arisen with black-box models such as neural networks,
  • work in groups and present their results together,
  • develop skills in collaborative interaction with peers.

Course information

Courses Lecture: Natural Language Processing for Business Analytics (2,5 SWS)

Exercise: Natural Language Processing for Business Analytics (2,5 SWS)

Credits 5 ECTS
Lecturers Prof. Dr. Patrick Zschech (Lecture)

Prof. Dr. Mathias Kraus (Exercise)

Time Block course in the first half of the semester.

Lecture: Thursday 8:45 – 10:15 (7 sessions à 90 min)

Exercise: Monday 15:00 – 18:00 (5 sessions à 180 min)

For further information see schedule.

Location This semester’s course (Winter 2022/2023) will be held exclusively online via Zoom. Please register for the course via StudOn.
Recommended prerequisites Basic knowledge in data analysis techniques, predictive modelling principles, statistics, and machine learning as taught, for example, in the Bachelor course “Business Analytics: Technologien, Methoden und Konzepte”.

Basic programming skills, preferably in Python.

Integration in curriculum First or third semester
Module compatibility Master International Information Systems (from 2018/19): Module in the section Information Systems – Data & Knowledge (Elective)

Master International Information Systems (from 2016/17): Module in the section Information Systems – Extension Courses (Elective)

Method of examination Project report and presentations, partly in groups
Grading procedure Project report (80%) and presentation (20%)
Module frequency Each winter term
Workload Contact hours: 75 h

Independent study: 75 h

Module duration 1 Semester
Teaching and examination language English
(Recommended) reading All relevant material will be provided during the course.

Registration

The number of participants is limited and requires a registration via StudOn. The registration period is between September 19 – October 20, 2022. If you register before October 13 there is a higher chance of participation with early bird confirmation. In case of excess demand, participants will be selected by drawing lots. Firm confirmation of registration at the latest until October 27, 2022.

Schedule (lecture, exercise, project seminar)

Please note that the schedule is not yet finalized and we reserve the option for changes.

CW Organization / Lecture Dates / Times Exercise Dates / Times
Course Registration September 19 – October 20, 2022
42
Opening Session
Thurs, Oct 20, 8:45-10:15
43
1) Intro NLP
Thurs, Oct 27, 8:45-10:15
44
2) Data preparation and linguistics
Thurs, Nov 3, 8:45-10:15
Intro Anaconda / Colab Python
Mon, Oct 31, 15:00-18:00
45
3) Rule-based approaches
Thurs, Nov 10, 8:45-10:15
Intro Linguistic
Mon, Nov 7, 15:00-18:00
46
Guest Lecture + Project Kick-off
Thurs, Nov 17, 8:45-10:15
Statistics + ML
Mon, Nov 14, 15:00-18:00
47
4) Statistical and ML approaches
Thurs, Nov 24, 8:45-10:15
Deep Learning I
Mon, Nov 21, 15:00-18:00
48
5) Artificial neural networks & embeddings
Thurs, Dec 1, 8:45-10:15
Deep Learning II
Mon, Nov 28, 15:00-18:00
49
6) Deep learning approaches
Thurs, Dec 8, 8:45-10:15
50
Consultation
Thurs, Dec 15, 8:45-10:15
51
Consultation
Thurs, Dec 22, 8:45-10:15
2
Consultation
Thurs, Jan 12, 8:45-10:15
3
Presentation Preliminary Results
Wedn, Jan 18, 9:00-14:30
4
Consultation
Thurs, Jan 26, 8:45-10:15
5
Consultation
Thurs, Feb 2, 8:45-10:15
6
Consultation/
Guest Lecture by Adidas
Monday, Feb 6, 9:15-11:15
10
Submission Project Seminar
Fri, March 10, 23:59
11
Final Presentation
Tues, March 14, 9:00-14:30

 

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Assistant Professorship for Intelligent Information Systems

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