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

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Natural Language Processing

Lecturers & Examiners: Prof. Dr. Ryan Cotterell

Number of participants limited to 200.

VVZ CR 2.5

Last Updated: 2026-02-05 15:35:54

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

Jacob Eisenstein: Introduction to Natural Language Processing (Adaptive Computation and Machine Learning series)

Learning Materials (Links)

Main link
Information
Literature
Introduction to Natural Language Processing - Jacob Eisenstein

General Information

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

Examination

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

Grade: 70% exam, 30% mandatory project.

Registration & Places

Max Places: 200

Course Components

Type	Title	Time & Place	Hours
lecture	Natural Language Processing The lecturers will communicate the exact lesson times of ONLINE courses.	Mon 12:00-14:00 (ON LI NE)	2 h weekly
exercise	Natural Language Processing The lecturers will communicate the exact lesson times of ONLINE courses.	Wed 13:00-14:00 (ON LI NE)	1 h weekly
independent project	Natural Language Processing	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
- Master Studies (Programme Regulations 2020)
  - Majors
    - Major in Machine Intelligence
      - Elective Courses
  - Minors
    - Minor in Machine Learning
- Master Studies (Programme Regulations 2009)
  - Focus Courses
    - Focus Courses in Information Systems
      - Focus Elective Courses Information Systems
    - Focus Courses General Studies
      - Focus Elective Courses General Studies
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.)
- Master Studies (Programme Regulations 2020)
  - Subject Specific Electives
- Master Studies (Programme Regulations 2014)
  - Specialization Areas and Electives
    - Statistical and Mathematical Courses
CAS in Computer Science
- Focus Courses and Electives
Doctoral Department of Computer Science (More Information at: )
- Doctoral and Post-Doctoral Courses
Data Science Master
- Core Courses
  - Core Electives
DAS in Data Science
- Specialisation Track
  - Machine Learning and Artificial Intelligence