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UVa CS 4501 Machine Learning for NLP

CS 4501 Machine Learning for Natural Language Processing

Table of Contents

1. Course Information

About final project

2. Course Description

Natural language processing (NLP) seeks to provide computers with the ability to process and understand human language intelligently. Examples of NLP techniques include (i) automatically translating from one natural language to another, (ii) analyzing documents to answer related questions or make related predictions, and (iii) generating texts to help story writing or build conversational agents. This course, consisting of one fundamental part and one advanced part, will give an overview of modern NLP techniques.

2.1 Topics

This course will mainly focus on applying machine learning (particularly, deep learning) techniques to natural language processing. NLP topics covered by this course

  1. Text classification
  2. Language modeling
  3. Word embeddings
  4. Machine translation and sequence-to-sequence models
  5. Some advanced topics: large-scale pre-trained language modelsing (e.g. BERT), natural language generation, interpretability in NLP

2.2 Prerequisites

2.3 Textbooks

Supplemental materials

3. Assignments and Final Project

4. Additional Information

Last updated on August 24, 2021