This is the new version of the course for the '19/20 Fall semester.
This course will present advanced problems and current state-of-the-art in the field of dialogue systems, voice assistants, and conversational systems (chatbots). After a brief introduction into the topic, the course will focus mainly on the application of machine learning – especially deep learning/neural networks – in the individual components of the traditional dialogue system architecture as well as in end-to-end approaches (joining multiple components together).
This course is a follow-up to the course NPFL123 Dialogue Systems, but can be taken independently – important basics will be repeated. All required deep learning concepts will be explained, but only briefly, so some machine learning background is recommended.
The course will be taught in English, but we're happy to explain in Czech as well.
Lectures: Thu 10:40 S1 (room changed!)
Labs: Thu 14:00 SW1
(labs will primarily take place online & in groups, with a few meetings during the semester)
To successfully finish this course, you'll need to pass a written exam (covering the lectures, especially parts mentioned in the summary) and participate in a lab project. More info on the exam is at the end of last lecture's slides.
Slides from past lectures will appear here:
The last lecture slides include information about the exam.
Lab assignments appear in the dedicated GitLab repository.