Natural Language Processing, 6 credits (TDDE09)
Språkteknologi, 6 hp
Main field of study
Information Technology Computer Science and Engineering Computer ScienceLevel
Second cycleCourse type
Programme courseExaminer
Marco KuhlmannDirector of studies or equivalent
Jalal MalekiCourse offered for | Semester | Period | Timetable module | Language | Campus | VOF | |
---|---|---|---|---|---|---|---|
6CDDD | Computer Science and Engineering, M Sc in Engineering | 8 (Spring 2017) | 1 | 2 | English | Linköping | v |
6CDDD | Computer Science and Engineering, M Sc in Engineering (Programming and Algorithms) | 8 (Spring 2017) | 1 | 2 | English | Linköping | v |
6CDDD | Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) | 8 (Spring 2017) | 1 | 2 | English | Linköping | v |
6CMJU | Computer Science and Software Engineering, M Sc in Engineering | 8 (Spring 2017) | 1 | 2 | English | Linköping | v |
6CMJU | Computer Science and Software Engineering, M Sc in Engineering (Programming and Algorithms Specialization) | 8 (Spring 2017) | 1 | 2 | English | Linköping | v |
6CMJU | Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) | 8 (Spring 2017) | 1 | 2 | English | Linköping | v |
6CITE | Information Technology, M Sc in Engineering | 8 (Spring 2017) | 1 | 2 | English | Linköping | v |
6CITE | Information Technology, M Sc in Engineering (Programming and Algorithms) | 8 (Spring 2017) | 1 | 2 | English | Linköping | v |
6CITE | Information Technology, M Sc in Engineering (AI and Machine Learning) | 8 (Spring 2017) | 1 | 2 | English | Linköping | v |
6MDAV | Computer Science, Master's programme | 2 (Spring 2017) | 1 | 2 | English | Linköping | v |
6MICS | Computer Science, Master's programme | 2 (Spring 2017) | 1 | 2 | English | Linköping | v |
Main field of study
Information Technology, Computer Science and Engineering, Computer ScienceCourse level
Second cycleAdvancement level
A1XCourse offered for
- Computer Science and Engineering, M Sc in Engineering
- Computer Science and Software Engineering, M Sc in Engineering
- Information Technology, M Sc in Engineering
- Computer Science, Master's programme
Entry requirements
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.
Prerequisites
Discrete mathematics. Good knowledge of programming, data structures, and algorithms. Basic knowledge of probability theory and optimisation. Previous courses in machine learning are recommended but no requirement for the course.
Intended learning outcomes
Natural Language Processing (NLP) develops techniques for the analysis and interpretation of natural language, a key component of smart search engines, personal digital assistants, and many other innovative applications. The goal of this course is to provide students with a theoretical understanding of and practical experience with the advanced algorithms that power modern NLP. The course focuses on methods that involve machine learning on text data. On completion of the course, the student should be able to:
- explain state-of-the-art NLP algorithms and analyse them theoretically
- implement NLP algorithms and apply them to practical problems
- design and carry out evaluations of NLP components and systems
- seek, assess and use scientific information within the area of NLP
Course content
State-of-the-art NLP algorithms for the analysis and interpretation of words, sentences, and texts. Relevant machine learning methods based on statistical modelling, combinatorial optimisation, and neural networks. NLP applications. Validation methods. NLP tools, software libraries, and data. NLP research and development.
Teaching and working methods
The course is given in the form of lectures, lab sessions, and seminars in connection with a minor project.
Examination
KTR1 | Optional written tests | U, G | 0 credits |
UPG1 | Project assignments | U, 3, 4, 5 | 2 credits |
LAB1 | Practical assignments | U, 3, 4, 5 | 2 credits |
TEN1 | Written examination | U, 3, 4, 5 | 2 credits |
The optional written tests give bonus points for the first attempt at the written examination. The final grade for the course is the median of the grades awarded for LAB1, TEN1, and UPG1. |
Grades
Four-grade scale, LiU, U, 3, 4, 5Other information
Supplementary courses:
Text Mining
Department
Institutionen för datavetenskapDirector of Studies or equivalent
Jalal MalekiExaminer
Marco KuhlmannEducation components
Preliminary scheduled hours: 48 hRecommended self-study hours: 112 h
Course literature
Additional literature
Compendiums
Lecture notes provided by the department.
Additional literature
Compendia
Lecture notes provided by the department.
KTR1 | Optional written tests | U, G | 0 credits |
UPG1 | Project assignments | U, 3, 4, 5 | 2 credits |
LAB1 | Practical assignments | U, 3, 4, 5 | 2 credits |
TEN1 | Written examination | U, 3, 4, 5 | 2 credits |
The optional written tests give bonus points for the first attempt at the written examination. The final grade for the course is the median of the grades awarded for LAB1, TEN1, and UPG1. |
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