Coursebooks

Deep Learning For Natural Language Processing

EE-608

Lecturer(s) :

Henderson James

Language:

English

Frequency

Every 2 years

Remarque

Next time: Fall 2019

Summary

The Deep Learning for NLP course provides an overview of neural network based methods applied to text. The focus is on models particularly suited to the properties of human language, such as categorical, unbounded, and structured representations, and very large input and output vocabularies.

Content

Models

 

Applications

Keywords

Machine Learning, Natural Language Processing, Neural Networks.

Learning Prerequisites

Required courses

Undergraduate level probability, linear algebra, and programming. 

Recommended courses

Courses on Machine Learning, Natural Language Processing (Human Language Technology, Computational Linguistics), or Artificial Intelligence would be useful.

Learning Outcomes

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

Assessment methods

Multiple.

In the programs

    • Semester
    • Exam form
       Multiple
    • Credits
      4
    • Subject examined
      Deep Learning For Natural Language Processing
    • Number of places
      40
    • Lecture
      28 Hour(s)
    • Practical work
      28 Hour(s)
    • Semester
    • Exam form
       Multiple
    • Credits
      4
    • Subject examined
      Deep Learning For Natural Language Processing
    • Number of places
      40
    • Lecture
      28 Hour(s)
    • Practical work
      28 Hour(s)

Reference week

 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German