Found 15 relevant results in 3.18s where lecturer="Ryan Cotterell"
This course serves as an introduction to various advanced topics in formal language theory.
In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning.
In this class, we bring together data science applicationsprovided by ETH researchers outside computer science andteams of computer science master's students. Two to threestudents will form a team working on data science/machinelearning-related research topics provided by scientists ina diverse range of domains such as astronomy, biology,social sciences etc.
In this class, we bring together data science applications provided by academic & industry stakeholders with teams of computer science master's students. Teams of students work on data science/machine learning-related research topics. Teams consist of two to three students, depending on the number of projects. Projects are collected by the lecturers and made available to choose from at the start.
Dependency parsing is a fundamental task in natural language processing. This seminar explores a variety of algorithms for efficient dependency parsing and their derivatioin in a unified algebraic framework.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
This course (e-learning module and face-to-face sessions) equips doctoral students with knowledge and tools to recognize, discuss and address ethical issues of their research.
Large language models have become one of the most commonly deployed NLP inventions. In the past half-decade, their integration into core natural language processing tools has dramatically increased the performance of such tools, and they have entered the public discourse surrounding artificial intelligence.
This course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
This graduate class, partly taught like a seminar, is designed to help you understand the philosophical underpinnings of modern work in natural language processing (NLP), most of which centered around statistical machine learning applied to natural language data.
Understand the philosophical underpinnings of language-based artificial intelligence.
Understand the philosophical underpinnings of language-based artificial intelligence.
Parsing context-free grammars is a fundamental problem in natural language processing and computer science more broadly. This seminar will explore a classic text that unifies many algorithms for parsing in one framework.