Course details


Deep learning for natural language processing

WS 2020 Dr. Elia Bruni
B.Sc modules:
CS-BWP-CL - (Computational) Linguistics
KOGW-WPM-CL - Computational Linguistics
M.Sc modules:
CC-MWP-CL - Computational Linguistics
CS-MWP-CL - (Computational) Linguistics
KOGW-MWPM-CL - Major subject Linguistics and Computational Linguistics

Doctorate program
Tue: 14-16
Wed: 10-12

Natural language processing (NLP) is the subfield of artificial intelligence that is concerned with making machines able to understand, process and use natural language. Because people communicate almost everything in language -- emails, text messages, language translation, virtual assistants, etc. -- NLP applications are everywhere. However, NLP is also hard: the ambiguities and noise inherent to human communication make traditional symbolic techniques frequently ineffective for representing and analysing language data. Recently, deep learning (or neural network) approaches have achieved a number of remarkable successes in NLP, leading to great commercial and academic interest in the field. In this course, you will gain a thorough understanding of modern neural network algorithms for NLP. Core techniques will not be treated as black boxes. On the contrary, you will get an in-depth understanding of what’s happening inside. To succeed in that, we expect your familiarity with the basics of linear algebra, calculus and probability theory. Through lectures, assignments and mini-projects, you will learn the necessary skills to design, implement, and understand your own neural network models. The course will be taught using PyTorch.