MachineLearningforLanguageData

3 Credits · Level: Graduate, Undergraduate

In the past few years, the advent of abundant computing power and data has catapulted machine learning to the forefront of a number of fields of research, including Linguistics and especially Natural Language Processing. At the same time, general machine learning toolkits and tutorials make handling ‘default cases’ relatively easy, but are much less useful in handling non-standard data, less studied languages, low-resource scenarios and the need for interpretability that is essential for drawing robust inferences from data. This course gives a broad overview of the machine learning techniques most used for text processing and linguistic research. The course is taught in Python, covering both general statistical ML algorithms, such as linear models, decision trees and ensembles, and current deep learning models, such as deep neural net classifiers, recurrent networks, transformers, sequence to sequence LLMs and contextualized continuous meaning representations. The course assumes good command of Python (ability to implement a program from pseudo-code) but does not require previous experience with machine learning.