EE-724 / 4 credits

Teacher: Popescu-Belis Andrei

Language: English

Remark: Next Time: Fall 2024


Every 2 years


The Human Language Technology (HLT) course introduces methods and applications for language processing and generation, using statistical learning and neural networks.



Human language technology, language engineering, neural networks, machine translation, information search and retrieval.

Learning Prerequisites

Recommended courses

At least one prior course in statistics, machine learning, or computational linguistics. Ability to use Python for simple projects based on existing libraries.

Learning Outcomes

By the end of the course, the student must be able to:

  • Explain the main neural network architectures used for human language technology
  • Categorize HLT tasks and list state-of-the-art solutions to solve them
  • Match in creative ways existing HLT building blocks to achieve new functionalities
  • Assess / Evaluate critically the impact of training data on the resulting systems, the related ethical issues, and bias correction strategies.

Assessment methods

Project report and oral presentation.


In the programs

  • Exam form: Multiple (session free)
  • Subject examined: Human language technology: applications to information access
  • Lecture: 28 Hour(s)
  • Practical work: 28 Hour(s)

Reference week

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