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252-3005-00L 5 Credits MSC , WBZ D-ITET , D-INFK , D-MATH

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Natural Language Understanding

Does not take place this semester. Takes place in HS20.

VVZ CR 2.5

Last Updated: 2026-02-05 15:42:00

Abstract

This course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.

Objective

The objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques.

Content

This course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.

Resources

Literature

Lectures will make use of textbooks such as the one by Jurafsky and Martin where appropriate, but will also make use of original research and survey papers.

Learning Materials (Links)

Main link
Information
Literature
SPEECH and LANGUAGE PROCESSING - Jurafsky and Martin

General Information

Language: English
Levels: MSC , WBZ
Frequency: Yearly recurring

Examination

Type: session examination
Mode: written 120 minutes
Aids: None

Grade: 70% exam, 30% mandatory project.

Course Components

Type	Title	Time & Place	Hours
lecture	Natural Language Understanding Does not take place this semester.	No time listed	2 h weekly
exercise	Natural Language Understanding Does not take place this semester.	No time listed	1 h weekly
independent project	Natural Language Understanding Does not take place this semester.	No time listed	1 h weekly

Offered In

Computational Science and Engineering Master
- Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
Computer Science Master
- Focus Courses
  - Focus Courses in Information Systems
    - Focus Elective Courses Information Systems
  - Focus Courses General Studies
    - Elective Focus Courses General Studies
CAS in Computer Science
- Focus Courses and Electives
Data Science Master
- Core Courses
  - Core Electives
DAS in Data Science
- Specialisation Track
  - Machine Learning and Artificial Intelligence