Natural Language Processing, 6 credits (TDDE09)

Main field of study

Information Technology, Computer Science and Engineering, Computer Science

Course level

Second cycle

Advancement level

A1X

Course 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

Grades

Four-grade scale, LiU, U, 3, 4, 5

Other information

Supplementary courses:
Text Mining 

Department

Institutionen för datavetenskap

Director of Studies or equivalent

Jalal Maleki

Examiner

Marco Kuhlmann

Education components

Preliminary scheduled hours: 48 h
Recommended self-study hours: 112 h

Course literature

Additional literature

Compendiums

Lecture notes provided by the department.