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
- 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, 15:00-18:0
|
|
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
|
Thurs, Jan 19, 9:00-14:30
|
||
|
4
|
Consultation
|
Thurs, Jan 26, 8:45-10:15
|
||
|
5
|
Consultation
|
Thurs, Feb 2, 8:45-10:15
|
||
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6
|
Consultation/Guest Lecture
|
Thurs, Feb 9, 8:45-10:15
|
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|
10
|
Submission Project Seminar
|
Fri, March 10, 23:59
|
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|
11
|
Final Presentation
|
Tues, March 14, 9:00-14:30
|