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Natural Language Processing
Last Updated: 2026-02-05 15:48:26
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 180 minutes
- Aids
- Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size.
Registration & Places
- Max Places
- 400
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture |
Natural Language Processing
From HS21 in the autumn semester.
|
|
2 h weekly |
| exercise | Natural Language Processing |
|
2 h weekly |
| independent project | Natural Language Processing | No time listed | 1 h weekly |
Offered In
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Application Area (Only necessary and eligible for the Master degree in Applied Mathematics. One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area.)
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Machine Learning (The list is not yet complete.)
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Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
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