Official course description, subject to change:
Preliminary info last published 30/11-20

Natural Language Processing and Deep Learning, BSc (Summer University)

Course info
Language:
English
ECTS points:
7.5
Course code:
BSNLPDL1KU
Participants max:
10
Offered to guest students:
yes
Offered to exchange students:
yes
Offered as a single subject:
no
Programme
Level:
Bachelor
Programme:
BSc in Software Development
Staff
Course semester
Semester
Forår 2022
Period
Summer 2021
Start
12 July 2021
End
27 August 2021
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract

This course is an introduction to Natural Language Processing and Deep Learning.

Description

This course is relevant for those students who wish to be exposed to applications of machine learning techniques.

Subjects:

  • An introduction to the major core topics in Natural Language Processing: language modelling, POS tagging and syntactic parsing. 
  • An introduction to basic Neural Networks for Natural Language Processing, and a corresponding Python-based framework. 
  • A project-based introduction to current Deep-Learning-based Natural Language Processing research.

Formal prerequisites

Before the course, the student should: 

  • be able to use basic algorithms and data structures when programming (equivalently, have passed the Algorithms and Data Structures course)
  • be able to use basic algorithmic techniques to design algorithms for a given problem 
  • be able to carry out basic n-dimensional matrix/vector computations by hand (e.g., matrix and vector products, addition, scalar multiplication, subtraction, etc.) 
  • hold a deep understanding of basic concepts in Statistics (e.g., distributions, summary statistics, law of large numbers, ?) 
  • be a strong Python programmer, but with some understanding of statically typed, pre-compiled languages (like C++ or Java).

Intended learning outcomes

After the course, the student should be able to:

  • Describe three core Natural Language Processing (NLP) tasks and implement basic respective computational approaches: language modelling, POS tagging, syntactic parsing.
  • Identify and formulate a task for NLP.
  • Identify why a given Neural Network architecture may be appropriate for an NLP task.
  • Design and carry out a sound experimental method for Neural-Network based NLP research.
  • Analyse the results of an NLP experiment.
  • Find, extract and explain results in the NLP and Deep Learning research literature relevant for a given problem.
Ordinary exam
Exam type:
C: Submission of written work, External (7-point scale)
Exam variation:
C1G: Submission of written work for groups