252-3005-00L  Natural Language Processing

SemesterAutumn Semester 2020
LecturersR. Cotterell
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 200.



Courses

NumberTitleHoursLecturers
252-3005-00 VNatural Language Processing
The lecturers will communicate the exact lesson times of ONLINE courses.
2 hrs
Mon12:00-14:00ON LI NE »
R. Cotterell
252-3005-00 UNatural Language Processing
The lecturers will communicate the exact lesson times of ONLINE courses.
1 hrs
Wed13:00-14:00ON LI NE »
R. Cotterell
252-3005-00 ANatural Language Processing1 hrsR. Cotterell

Catalogue data

AbstractThis 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.
ObjectiveThe 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.
ContentThis 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.
LiteratureJacob Eisenstein: Introduction to Natural Language Processing (Adaptive Computation and Machine Learning series)

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits5 credits
ExaminersR. Cotterell
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationGrade: 70% exam, 30% mandatory project.
Written aidsNone
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main linkInformation
LiteratureIntroduction to Natural Language Processing - Jacob Eisenstein
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

Places200 at the most
PriorityRegistration for the course unit is until 24.09.2020 only possible for the primary target group
Primary target groupData Science MSc (261000)
Computer Science MSc (263000)
Doctorate Computer Science (264002)
DAS ETH in Data Science (266000)
CAS ETH in Computer Science (269000)
Computational Science and Engineering MSc (438000)
Waiting listuntil 03.10.2020

Offered in

ProgrammeSectionType
CAS in Computer ScienceFocus Courses and ElectivesWInformation
DAS in Data ScienceMachine Learning and Artificial IntelligenceWInformation
Data Science MasterCore ElectivesWInformation
Doctoral Department of Computer ScienceDoctoral and Post-Doctoral CoursesWInformation
Computer Science MasterElective CoursesWInformation
Computer Science MasterFocus Elective Courses General StudiesWInformation
Computer Science MasterFocus Elective Courses Information SystemsWInformation
Computer Science MasterMinor in Machine LearningWInformation
Computational Science and Engineering MasterElectivesWInformation
Statistics MasterStatistical and Mathematical CoursesWInformation
Statistics MasterSubject Specific ElectivesWInformation