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Natural Language Processing (CUGS Core)2023VT
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Course plan
Aim
Natural Language Processing (NLP) develops methods for making human language accessible to computers. This course aims to provide you with a theoretical understanding of and practical experience with the advanced algorithms that power modern NLP. The course focuses on methods based on deep neural networks.
Recommended for
Doctoral students in computer science and other disciplines with previous experience in machine learning
The course was last given
Spring 2021. The course is an adaptation of a well-established Master's-level course targeted at students in engineering (TDDE09).
Goals
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
Prerequisites
* discrete mathematics, calculus, linear algebra, probability theory
* good knowledge of programming, data structures, and algorithms
* machine learning
The lab series for the course uses Python.
Contents
* state-of-the-art algorithms for the analysis and interpretation of natural
language
* relevant machine learning methods with a focus on deep neural networks
* validation methods
* NLP applications
* NLP tools, software libraries, and data
* NLP research and development
Organization
The course is given in the form of lectures, computer labs, and supervision in connection with a project.
Literature
The reading for this course consists of individual sections from the following
books:
Jacob Eisenstein. Introduction to Natural Language Processing. MIT Press, 2019.
Pre-print version available online
Daniel Jurafsky and James H. Martin. Speech and Language Processing. An
Introduction to Natural Language Processing, Computational Linguistics, and
Speech Recognition. Draft chapters in progress, December 2021.
Lecturers
Marco Kuhlmann
Examiner
Marco Kuhlmann
Examination
The course consists of the following modules:
* Practical assignments, 3 credits (U, G)
* Project assignments, 3 credits (U, G)
Alternatively, you can replace the project module with an extended set of
practical assignments.
To pass the course, you must pass both modules.
Credit
6 credits
Organized by
Department of Computer and Information Science, Linköping University
Comments
This is a CUGS Core course.
Page responsible: Director of Graduate Studies
