Machine Learning for Natural Language Processing (NLP)
Objectives
– Understand the foundations of recent NLP models (language models, transformers, GPT)
– Implement natural language processing pipelines
– Design solutions for text information extraction
Course outline
The course will introduce the main notions of NLP and detail machine learning based approaches to modern NLP, going through the following: word representation, text classification, word tagging, lan-guage modeling, transformers and large language models, text generation.
The courses will tightly mix lectures and guided hands-on practice that will be complemented by small personal projects pursuing the guided hands-on practice sessions.
Prerequisites
– Foundations of machine learning (probability/statistics, optimization, gradient descent, loss function, etc.)
– Good knowledge of Python
– Familiarity with tensirflow/keras and/or pyTorch