EE-608 / 4 credits

Teacher: Henderson James

Language: English

Remark: Next time: Fall 2021


Frequency

Every 2 years

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

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:

  • Identify appropriate deep learning architectures for different natural language processing tasks.
  • Apply appropriate training and evaluation methodology to such models on large datasets using existing packages.

Assessment methods

Multiple.

In the programs

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

Reference week