Tentalabbet

TDDE09 · 6 credits · Advanced level (A1F) · Informationsteknologi, Datateknik, Datavetenskap

Natural Language Processing

Active · next start VT 2027

57%

passed the latest regular sitting

UPG5 · 27 Mar 2026 · 42 students

63%

passed the regular sittings, all sittings over the last 5 years

UPG5 · 70 students

92%

Harder than 92% of courses at advanced level

Compared with 285 courses at the same level, ordinary exams over the last three academic years. 70 students.

Part of 7 programmes

Hand-in or lab

UPG5 Project assignments (individual part)

  • 5
  • 4
  • 3
  • U

Main rounds

63% passed · 70 students

  • 57%42
  • 71%28

Later rounds

85% passed · 27 students

  • 100%5
  • 100%14
  • 50%8
  • Pass a later round: 85% (27 students), compared with 63% (70 students) on the ordinary exam
  • Get 4 or 5: 47% (97 students)
Show small sittings (1)

Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.

  • 100%4

Other modules

Partial exams, hand-ins and labs. They are not part of the figures at the top.

Hand-in or lab

UPG2 Project assignments

  • 5
  • 4
  • 3
  • U

Main rounds

69% passed · 157 students

  • 53%66
  • 82%39
  • 79%52

Later rounds

100% passed · 28 students

  • 100%20
  • 100%8
  • Pass a later round: 100% (28 students), compared with 69% (157 students) on the ordinary exam
  • Get 4 or 5: 57% (185 students)
Show small sittings (6)

Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.

  • 100%1
  • 100%2
  • 100%2
  • 100%1
  • 100%4
  • 100%1

Hand-in or lab

LAB2 Practical assignments

  • 5
  • 4
  • 3
  • U

Main rounds

87% passed · 155 students

  • 91%66
  • 84%38
  • 84%51

Later rounds

100% passed · 7 students

  • 100%7
  • Pass a later round: 100% (7 students), compared with 87% (155 students) on the ordinary exam
  • Get 4 or 5: 30% (162 students)
Show small sittings (4)

Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.

  • 100%4
  • 100%2
  • 0%2
  • 100%1

Hand-in or lab

LAB3 Laboratory assignments

  • 5
  • 4
  • 3
  • U

Main rounds

60% passed · 40 students

  • 60%40

Later rounds

70% passed · 53 students

  • 80%5
  • 72%18
  • 57%7
  • 70%10
  • 100%6
  • 43%7
  • Pass a later round: 70% (53 students), compared with 60% (40 students) on the ordinary exam
  • Get 4 or 5: 15% (93 students)
Show small sittings (4)

Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.

  • 50%2
  • 100%1
  • 100%3
  • 100%3

Hand-in or lab

UPG3 Theory assignments

  • G
  • U

Main rounds

88% passed · 67 students

  • 97%37
  • 77%30

Later rounds

80% passed · 15 students

  • 67%9
  • 100%6
  • Pass a later round: 80% (15 students), compared with 88% (67 students) on the ordinary exam
Show small sittings (2)

Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.

  • 100%2
  • 75%4

Hand-in or lab

UPG4 Project assignments (group part)

  • 5
  • 4
  • 3

Main rounds

100% passed · 76 students

  • 100%46
  • 100%30

Later rounds

No sittings with at least 5 students.

  • Get 4 or 5: 71% (76 students)
Show small sittings (1)

Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.

  • 100%1

About the course

Aim

Natural Language Processing (NLP) enables computers to work with human language. In this course, you will explore the core models and algorithms that underpin modern NLP, with a strong emphasis on practical experience. The course focuses particularly on large language models (LLMs) and the development of chatbots.

On completion of the course, the student should be able to:

  • explain central concepts, models and algorithms of NLP
  • implement NLP algorithms and apply them to realistic problems
  • evaluate NLP components and systems with appropriate methods
  • identify, assess and make use of NLP research literature

Content

General content

  • concepts, models and algorithms of natural language processing
  • relevant machine learning methods
  • validation methods
  • NLP applications
  • NLP tools, software libraries, and data
  • NLP research and development
  • societal, environmental and ethical aspects of NLP

Focus: Large language models and chatbots

  • tokenisation (BPE), embeddings, word vectors
  • LLMs based on the Transformer architecture (GPT, BERT)
  • development of LLMs and chatbots: data, pretraining, finetuning
  • current research and development

Lab series

In the lab series, the students implement, train and evaluate a complete chatbot based on the GPT architecture.

Examiner
Marco Kuhlmann
Scheduled hours
42 h
Main field of study
Informationsteknologi, Datateknik, Datavetenskap
Syllabus on Studieinfo ↗