Course details


Deep learning for natural language processing

WS 2023 Dr. Elia Bruni Hybrid
B.Sc modules:
CS-BWP-AI - Artificial Intelligence
CS-BWP-CL - (Computational) Linguistics
CS-BWP-NI - Neuroinformatics
KOGW-WPM-CL - Computational Linguistics
KOGW-WPM-KI - Artificial Intelligence
KOGW-WPM-NI - Neuroinformatics
M.Sc modules:
CC-MWP-AI - Artificial Intelligence
CC-MWP-CL - Computational Linguistics
CC-MWP-NI - Neuroinformatics
CS-MWP-AI - Artificial Intelligence
CS-MWP-CL - (Computational) Linguistics
CS-MWP-NI - Neuroinformatics

CS-BW - Bachelor elective course
CS-MW - Master elective course
Doctorate program
Tue: 12-14
Wed: 8-10

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. Prerequisites: Basic programming skills (preferably Python, but other programming languages are fine too); Basic math (Linear algebra and calculus).