Machine Translation
Master the principles and technologies behind automated language translation systems through comprehensive theory and practice.
Master the principles and technologies behind automated language translation systems through comprehensive theory and practice.
This advanced course explores the fundamentals of machine translation technology, from traditional statistical approaches to modern neural network solutions. Students learn about the challenges of translating between natural languages, evaluation methods, and state-of-the-art neural machine translation architectures. The curriculum covers both theoretical foundations and practical applications, enabling learners to understand and implement various machine translation approaches.
4.5
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What you'll learn
Understand machine translation fundamentals and challenges
Master statistical translation methods and models
Learn neural network approaches to translation
Implement attention-based translation systems
Evaluate translation quality using industry metrics
Explore multilingual translation architectures
Skills you'll gain
This course includes:
7.6 Hours PreRecorded video
30 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 7 modules in this course
This comprehensive course covers the evolution and current state of machine translation technology. Starting with fundamental concepts and challenges in language translation, it progresses through statistical methods to modern neural approaches. The curriculum includes detailed coverage of evaluation metrics, model architectures, and practical implementation considerations. Students learn both theoretical foundations and practical applications of machine translation systems.
Introduction to the basics of Machine Translation
Module 1 · 53 Minutes to complete
Language
Module 2 · 3 Hours to complete
Evaluation
Module 3 · 4 Hours to complete
Statistical Machine Translation
Module 4 · 6 Hours to complete
Neural Network Models
Module 5 · 3 Hours to complete
NMT
Module 6 · 3 Hours to complete
More NMT
Module 7 · 4 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
Assistant Professor at the Karlsruhe Institute of Technology
Jan Niehues is an Assistant Professor at the Department of Data Science and Knowledge Engineering at Maastricht University, having previously earned his doctoral degree from the Karlsruhe Institute of Technology in 2014, focusing on "Domain Adaptation in Machine Translation." His research spans various aspects of Machine Translation and Spoken Language Translation, and he has contributed to several international projects, including the German-French Quaero project and the EU H2020 projects QT21 and Elitr. Jan has also been involved in organizing the International Workshop on Spoken Language Translation (IWSLT) and has conducted research at esteemed institutions such as KIT, Carnegie Mellon University, and LIMSI/CNRS in Paris.
Pioneering Professor in AI and Speech Translation
Alexander Waibel is a distinguished professor of Computer Science at both Carnegie Mellon University and the Karlsruhe Institute of Technology, where he directs the International Center for Advanced Communication Technologies. Renowned for his groundbreaking work in artificial intelligence, machine learning, multimodal interfaces, and speech translation systems, Waibel developed early neural network-based systems, including the Time Delay Neural Network (TDNN), which was the first convolutional neural network. His innovations have led to the creation of various multimodal interfaces and cross-lingual communication systems, such as the first consecutive and simultaneous speech translation systems. Waibel has founded over ten companies to translate academic research into practical applications, including Jibbigo, the first mobile speech translator. With approximately 1,000 publications and numerous awards, he is a member of the National Academy of Sciences of Germany and a Fellow of the IEEE, holding degrees from MIT and CMU.
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4.5 course rating
159 ratings
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