Overview

By Jinho D. Choi (2023 Edition)

Natural Language Processing (NLP) is a vibrant field in Artificial Intelligence that seeks to create computational models to understand, interpret, and generate human language. NLP technology has become deeply ingrained in our daily lives through various applications, evolving at an unprecedented pace. Understanding how NLP works enables you to maximize the utilization of these applications, ultimately enhancing your lifestyle.

This course focuses on establishing a solid foundation in the core principles essential for modern NLP techniques. Starting with the basics of text processing, you will learn how to manipulate text to enhance data quality for developing NLP models. Next, we will delve into language modeling that enables computational systems to understand and generate human language and explore vector space models that convert human language into machine-readable vector representations.

Moving forward, we will cover distributional semantics, a technique for creating word embeddings based on their global contextual usage, and adapt them for sequence modeling to tackle NLP tasks that are inherently structured around sequences of words. We will also delve into contextual representations that capture the subtleties and nuances of language by considering local context. Finally, we will explore cutting-edge topics, including large language models and their effects on NLP tasks and applications.

Throughout the course, several quizzes and Python programming assignments will further deepen your understanding of the concepts and the practice of NLP. By the end of the term, you can expect to possess the knowledge and skills necessary to navigate the swiftly evolving landscape of NLP.

Prerequisites

• Introduction to Python Programming

• Introduction to Machine Learning

Notations

• Each section has its own set of references. We highly recommend you read the ones marked with asterisks (*), as they provide an in-depth understanding of those subjects.