
PERCEPTION
Just like humans, artificial intelligence has to perceive – i.e. to sense/measure/observe the real world. It also needs to communicate with humans both at the input and at the output.
Without a connection to the real world, AI is of no use. It needs to see the world around as well as to understand our language and talk to people properly, carrying a meaningful and purposeful dialogue.
The Natural Language Processing course offers a broad and yet detailed and technical overview of the several important areas in NLP such as language modelling, word and sentence embeddings, information retrieval or machine translation. To handle the softness of the observations and uncertainty in the data, NLP has been using statistical methods and (deep) neural networks ever since. To allow for expressing what we, users of language, feel as rules, symbolic methods have always been at hand, including exact handling of ambiguities. For many NLP tasks, data are plentiful and easy to process (plaintext!) and yet immediately demonstrate the complex fabric of the language and our communication. Be sure to get your hands dirty with the data, in the NLP course.
This Dialogue Systems course introduces the architecture of spoken dialogue systems, voice assistants and conversational systems (chatbots). It discusses the main components of dialogue systems (speech recognition, language understanding, dialogue management, language generation and speech synthesis) and shows alternative approaches to their implementation.
The Computer Vision Methods course addresses some key challenges in visual perception including how to find images with similar semantic and not always the same visual content (image retrieval), how to pair images (image matching) and how to follow moving objects (visual tracking). The course also discusses some machine learning approaches used in the present computer vision world.
Courses
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Computer Vision Methods
FEE CTU (Summer)
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Dialogue Systems
MFF CUNI (Summer)
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Large Language Models
MFF CUNI (Summer)
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Natural Language Processing
MFF CUNI (Summer)
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Personalized Machine Learning
FIT CTU (Winter)
STILL SOME QUESTIONS?
Contact us at minor@prg.ai and we will get back to you shortly.
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FEE CTU
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Artificial Intelligence in Robotics
B4M36UIR/BE4M36UIR Winter
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Autonomous Robotics
B3M33ARO1/BE3M33ARO1 Summer
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Computer Vision Methods
B4M33MPV/BE4M33MPV Summer
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Functional Programming
B4B36FUP Summer
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Introduction to Artificial Intelligence
B4B36ZUI Summer
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Julia for optimization and learning
B0B36JUL Winter
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Pattern Recognition and Machine Learning
B4B33RPZ/BE5B33RPZ Winter
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FIT CTU
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FSV CUNI
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MFF CUNI
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Artificial Intelligence I
NAIL069 Winter
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Artificial Intelligence II
NAIL070 Summer
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Deep Learning
NPFL138 Summer
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Deep Reinforcement Learning
NPFL139 Summer
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Dialogue Systems
NPFL123 Summer
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Introduction to Artificial Intelligence
NAIL120 Summer
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Introduction to Machine Learning with Python
NPFL129 Winter
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Large Language Models
NPFL140 Summer
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Modern Algorithmic Game Theory
NOPT021 Winter
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Natural Language Processing
NPFL124 Summer
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