Machine learning for statistical NLP: introduction

This course introduces students to the principles and practices of machine learning in Natural Language Processing (NLP). It provides a solid foundation in the statistical and mathematical concepts underlying modern language technologies, combining theoretical understanding with practical experience in data analysis and model development.

Students learn how to represent linguistic data for machine learning, design data pipelines, and apply standard algorithms such as support vector machines, logistic regression, and basic neural networks. The course emphasizes hands-on experimentation using established NLP and machine learning toolkits, as well as good scientific practices and documentation.

By the end of the course, students will be able to:

Understand how linguistic data is used in NLP applications and the mathematical foundations behind them.

Apply and evaluate common statistical and machine learning techniques for NLP tasks.

Develop and document small-scale NLP systems using modern tools.

Critically assess and compare approaches to solving NLP problems.