Official course description:

Full info last published 19/11-20
Course info
Language:
English
ECTS points:
15
Course code:
BSSEYEP1KU
Participants max:
71
Offered to guest students:
no
Offered to exchange students:
no
Offered as a single subject:
no
Programme
Level:
Bachelor
Programme:
BSc in Data Science
Staff
Course manager
Associate Professor, Head of study programme
Teacher
PhD student
Teacher
PhD student
Teacher
PhD student
Teacher
Postdoc
Course semester
Semester
Forår 2021
Start
1 February 2021
End
14 May 2021
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract
After completing this course, students are able to tackle a challenge in Natural Language Processing, implement a solution, and critically assess their solution in the light of robustness and state-of-the art.
Description

An introduction to major topics in Natural Language Processing and relevant related topics (traditional and neural-network based methods to, e.g., language modeling, classification, sequence processing).
An introduction to deep neural networks for Natural Language Processing, including representation learning, and an implementation in a corresponding Python-based framework. 

In this project-based course, students are going to work on a real-world problem using natural language processing technology. The focus of the theoretical part of this class is on natural language processing and deep learning. Social implications of data handling will be discussed as well. 

The course has two parts: a series of lectures and exercises with a possibility for guest lecturers followed by project-based work in groups.

Amongst others, the following topics will be covered:
  • Introduction to NLP and DL, what makes language so difficult; traditional versus neural approaches
  • Data Science at the command line; obtaining and preprocessing data
  • Language Models, Neural LMs
  • Classification for NLP, Embeddings
  • Sequence predictions for NLP, RNNs and LSTMs
  • Ethical considerations of machine learning


Formal prerequisites
Intended learning outcomes

After the course, the student should be able to:

  • Discuss, clearly explain, and reflect upon central concepts, algorithms, and challenges in natural language processing (NLP) and deep learning (DL).
  • Organize, plan, and carry out collaborative work in a smaller project group.
  • Obtain, scrub, explore and preprocess a wide range of relevant raw data for a given problem. Identify and analyze the relevant options for data collection and preprocessing and select the most suitable ones.
  • Design and implement a sound experiment in NLP.
  • Distinguish and evaluate the advantages of different design choices or approaches to the same task (e.g., traditional versus deep-learning based solutions).
  • Evaluate the achieved solution and carry out a detailed error analysis, relating the findings back to the overall problem domain
  • Explain in writing (project group report) adhering to academic standards in writing.
  • Succinctly present the results of the project, discuss findings and limitations
  • Explain and reflect upon ethical considerations that arise in the deployment of language technology
Learning activities

The course has two parts: a series of lectures and exercises with a possibility for guest lecturers followed by project-based work in groups.

Course literature

The course literature is published in the course page in LearnIT.

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 5%
  • Lectures: 25%
  • Exercises: 10%
  • Assignments: 5%
  • Project work, supervision included: 50%
  • Exam with preparation: 5%
Ordinary exam
Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.
Exam submission description:
Submit 2 files:
- Project report as PDF file
- Source code as .zip file
Group submission:
Group
  • 4 people
Exam duration per student for the oral exam:
20 minutes
Group exam form:
Mixed exam 1 : Individual and joint student presentation followed by an individual and a group dialogue. The students make a joint presentation followed by a group dialogue. Subsequently the students are having individual examination with presentation and / or dialogue with the supervisor and external examiner while the rest of the group is outside the room.


reexam
Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D22: Submission with following oral exam supplemented by the submission.
Exam submission description:
Submit 2 files:
- Project report as PDF file
- Source code as .zip file
Exam duration per student for the oral exam:
20 minutes

Time and date