CAS Natural Language Processing
Machine translation, information extraction, question answering and non-sense natural language generation performed by NLP algorithms are already part of everyday life. However, the new semantic understanding capabilities seen in recent trained transformer models like Chat-GPT and LaMDA not only expand the NLP application space enormously, but also represent possible first steps towards consciousness and mind outside human and animal nerve systems.
The program targets practitioners who aim for an overview of the NLP domain with focus on recent developments (deep learning models) and hands-on learning. Programming skills and some machine learning experiences are prerequisites.
The CAS format is designed to align with the participants’ main professional and study activities. The workload is on average about 20% of a full-time position over a year. The teaching and learning approaches are team and discussion oriented, aimed at developing practical competency. All modules can also be attended online.
|Degree||Certificate of Advanced Studies in Natural Language Processing NLP University of Bern (CAS NLP Unibe)|
|Length||August 2023 – July 2024|
|Flexible entry possible||No|
|Single module visitable||Yes|
|Place||University of Bern; Mürren, Bernese Oberland (Module 6); Mediterranean coast (Module 3)
Due to the pandemic situation and the related official requirements, we would like to point out that the modules are always accessible online.
|Admission||The prerequisite for admission to the programme is a degree from a university or a university of applied sciences and basic knowledge of mathematics and programming to the extent of an introductory lecture at a university or university of applied sciences.|
|Special Offer||Employees and Students of the University of Bern: CHF 5’600|
|Organising institutions||Mathematical Institute|
About the program
The CAS Natural Language Processing is a university study program leading to a “Certificate of Advanced Studies in Natural Language Processing” awarded by the University of Bern.
The field Natural Language Processing belongs to both computational linguistics as its engineering domain and artificial intelligence as an increasingly important subdomain. The applications based on deep neural networks have reached a
performance level which cannot be ignored by any field that is processing natural languages.
The program is organised into six modules, running over 18 course days from August to January and targets practitioners who aim for an overview of the NLP domain with focus on recent developments (deep learning models) and hands-on learning. The difficulty is at a university master level and assumes own basic machine learning experience, programming skills and a higher education degree.
Course competence is developed throughout six modules and a CAS project work. On completion the graduates will (be able to):
- have an overview of the NLP domain and common applications
- be able to perform relevant preprocessing tasks needed for advanced NLP
- be able to understand neural networks and practice them on own NLP applications
- be able to understand transformers and practice transfer learning with transformers for own applications
- know discussions related to philosophical and ethical aspects around NLP and artificial intelligence
- be familiar with active research in the NLP domain.
If there are free places, modules can be attended individually.
Module 1: NLP Fundamentals
Block module. In this module, linguistics and machine learning concepts are introduced and an overview of the NLP field and common applications is given.
Module 2: Preprocessing and basic analysis
Block module. In this module, participants learn to perform basic preprocessing and analysis of natural language.
Module 3: Neural networks
In this module, participants learn how neural networks work, are trained, tuned, assessed and applied for NLP tasks
Module 3 takes place at the mediterranean coast. Full pension hotel accommodation is included in the CAS fee.
Module 4: Transformers
Participants study transformers and learn why they have changed the NLP field.
Module 5: Philosophical and ethical aspects of NLP
In this module, participants study and discuss ethical and philosophical aspects related to machines being capable of natural language processing.
Module 6: Frontier and applications
This module consolidates the knowledge obtained from previous modules to focus on prominent NLP topics and applications.
Module 6 takes place in the wonderful historic hotel Regina in the ski resort Mürren (Bernese oberland), only about two train hours from Bern. Full pension hotel accommodations are included in the CAS fee.
The duration of all modules corresponds to approximately 20 classroom hours each and module work (expected eff ort is 30 hours), with each complete module qualifying for 2 ECTS points. The expected workload for the CAS Project is 120 hours. Computational resources are offered.
|ExWi||Exakte Wissenschaften (Sidlerstrasse 5)|
|UniM||Uni Mittelstrasse (Mittelstrasse 43)|
|HG||Main Building (Hochschulstrasse 4)|
Learn everything you need to know about the CAS in Natural Language Processing. One introduction is mandatory, remote participation is possible.
|2023-03-27||18:15 - 20:00||HG 212||Introduction to CAS NLP||PD Dr. S. Haug||Link to Zoom Meeting will appear here|
|2023-06-19||18:15 - 20:00||HG 212||Introduction to CAS NLP||PD Dr. S. Haug||Link to Zoom Meeting will appear here|
Prepare yourself for the CAS Modules. We offer the following introductionary courses to refresh your knowledge.
Introduction to Programming (Python)
|2023-08-14||09:15 - 17:00||UT
|Dr. K. Sipos||This course is intended for CAS ADS students but CAS NLP participants who would like to refresh their Python programming knowledge are welcome too.||Link to Ilias course will appear here|
Mathematical Methods for Data Science and Machine Learning
|2023-08-15 - 2023-08-18
|09:15 - 17:00||UT
|Dr. K. Sipos||This course is intended for CAS AML students, but interested CAS NLP participants who wish to deepen their mathematical knowledge and learn about machine learning mathematics are welcome too.||Link to Ilias course will appear here|
All Information about Modules 1-6.
Course materials are accessed via the Ilias learning platform.
|M1 NLP Fundamentals||2023-08-22 - 2023-08-25
|09:15 - 12:00||UT
|Prof. Dr. T. Hodel, Dr. C. Schneider|
|M2 Preprocessing and basic analysis||2023-08-29 - 2023-09-01
|09:15 - 12:00||UniM
|Dr. C. Schneider|
|M3 Neural Networks||2023-10-09 - 2023-10-13
|08:30 - 12:30
17:00 - 19:00
|TBD (Mediterranean Coast)||PD Dr. S. Haug, Dr. M. Vladymyrov||On Friday the module ends at 12:30|
|M4 Transformers||Every Friday
2023-10-20 until 2023-12-15
|15:15 - 17:00||ExWi B77||M.Sc. J. Niklaus, M. A. P. Ströbel|
|M5 Philosophical and ethical aspects of NLP||Every Friday
2023-10-20 until 2023-12-15
|13:15 - 15:00||ExWi B77||Prof. Dr. Dr. C. Beisbart|
|M6 Frontier and applications||2024-01-29 - 2024-02-02
|08:30 - 12:30
17:00 - 19:00
|Hotel Regina Mürren
|PD Dr. S. Haug, Dr. M. Vladymyrov||On Friday the module ends at 12:30|
|Final Project deadline|
Events and other important dates
|2023-08-25||TBD||TBD||Come together and have a drink or two|
CAS Completion Notification
|2024-07-31||Be informed that you have completed the CAS programme|
CAS Graduation Celebration
|late August / early September||TBD||TBD||Celebrate your Graduation!|
Organising institution and faculty
The Certificate of Advanced Studies (CAS) in Natural Language Processing (NLP) is offered by the Mathematical Institute.
- Prof. Dr. Jan Draisma
- Prof. Dr. Tobias Hodel
- Prof. Dr. Paolo Favaro
- PD Dr. Sigve Haug (director of studies)
- Prof. Dr. Christiane Tretter (chair)
- Prof. Dr. Thomas Wihler
- Prof. Dr. Dr. Claus Beisbart
- PD Dr. Sigve Haug
- Prof. Dr. T. Hodel
- Dr. C. Schneider
- Dr. Kinga Sipos
- Dr. Mykhailo Vladymyrov
- M.Sc. Joel Niklaus,
- M. A. Phillip Ströbel
- et al.
Aimed at students and professionals from the public/private sector that hold a degree from a university or a university of applied sciences (e.g. BSc, MSc, PhD).
SUITABLE AND INTENDED FOR PRACTITIONERS AND RESEARCHERS ► Gain an overview of the Natural Language Processing domain with a focus on recent developments (deep learning models) and hands-on learning.
Standard data sets are provided, but participants are encouraged to bring or acquire their own. lf you have any questions regarding whether this program could work for you, please do not hesitate to contact us.
Registered participants will receive acceptance confirmation by email and will be invited to one of the next information events. Attendance to one information event is mandatory. Participants can cancel their registrations before the deadline without any costs. After the deadline the regulations apply. Individual modules and electives can be attended before the registration. Please contact PD Dr. Sigve Haug for further information.
Registration opens in November and a maximum of 20 registrations can be accepted each year. Registrations are processed in the order of arrival. The CAS can only be offered if there are sufficient registrations by the deadline.
Application and tuition fees
Per year there are 20 places. Registrations are accepted in the order they arrive. A waiting list is maintained.
Regular CAS program: CHF 9'600.-
Employees & Students of University of Bern: CHF 5'600.-
Payment in installments is possible.
lnclusive of all modules, performance assessments, certificates, materials & teaching platforms, coffee breaks, full pension hotels (Module 3 and Module 6) and diploma apero.
Participants must supply their own laptops.
lf there are free places, modules can be attended individually. Prices are CHF 300.- per half day. Individual modules are accredited with certificates which are accumulated for the full CAS NLP.
DI & DO
CAS in Advanced Machine Learning
This CAS AML offers the opportunity to complement your data science competences with a formal deepening and broadening of knowledge and skills on machine learning and artificial intelligence.
CAS in Applied Data Science
Data science is a discipline consisting of applied mathematics, statistics, computer science, ethics and subject specific knowledge in application areas. The content covers a full cycle from data acquisition planning, description and visualisation of data, inference, machine learning best practices and ethics.