{"id":null,"code":"TKO_8965","name":{"valueFi":"Deep Learning in Human Language Technology","valueEn":"Deep Learning in Human Language Technology","valueSv":"Deep Learning in Human Language Technology"},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[],"createdAt":1790534310079,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"After completing the course, the student will be able to\r\n- understand basic and advanced deep neural network architectures and their application to various tasks in natural language processing\r\n- select appropriate language resources and deep learning models and fine-tune state-of-the-art models for a range of tasks involving natural language\r\n- understand and explain the capabilities and limitations of deep learning-based models and concepts such as transfer learning, multi- and cross-lingual models, and large-scale pre-training\r\n- independently implement multi-stage natural language processing systems combining several task-specific models","valueEn":"After completing the course, the student will be able to\r\n- understand basic and advanced deep neural network architectures and their application to various tasks in natural language processing\r\n- select appropriate language resources and deep learning models and fine-tune state-of-the-art models for a range of tasks involving natural language\r\n- understand and explain the capabilities and limitations of deep learning-based models and concepts such as transfer learning, multi- and cross-lingual models, and large-scale pre-training\r\n- independently implement multi-stage natural language processing systems combining several task-specific models","valueSv":"After completing the course, the student will be able to\r\n- understand basic and advanced deep neural network architectures and their application to various tasks in natural language processing\r\n- select appropriate language resources and deep learning models and fine-tune state-of-the-art models for a range of tasks involving natural language\r\n- understand and explain the capabilities and limitations of deep learning-based models and concepts such as transfer learning, multi- and cross-lingual models, and large-scale pre-training\r\n- independently implement multi-stage natural language processing systems combining several task-specific models"}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"The course introduces advanced neural network architectures as used specifically for textual data. The course focuses both on the modelling aspects, underlying the latest NN architectures, as well as on their practical implementation on typical classes of natural language processing task. Exercises and the course project emphasize practical skills in training neural networks to address a range of tasks and the use of deep learning-based models as components of practical systems and provide students with the skills to train models in GPU-accelerated environments.","valueEn":"The course introduces advanced neural network architectures as used specifically for textual data. The course focuses both on the modelling aspects, underlying the latest NN architectures, as well as on their practical implementation on typical classes of natural language processing task. Exercises and the course project emphasize practical skills in training neural networks to address a range of tasks and the use of deep learning-based models as components of practical systems and provide students with the skills to train models in GPU-accelerated environments.","valueSv":"The course introduces advanced neural network architectures as used specifically for textual data. The course focuses both on the modelling aspects, underlying the latest NN architectures, as well as on their practical implementation on typical classes of natural language processing task. Exercises and the course project emphasize practical skills in training neural networks to address a range of tasks and the use of deep learning-based models as components of practical systems and provide students with the skills to train models in GPU-accelerated environments."}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study methods","valueSv":""},"content":{"valueFi":"Hands-on programming exercises, electronic exam, course project carried out individually or in pairs","valueEn":"Hands-on programming exercises, electronic exam, course project carried out individually or in pairs","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Course unit methods","valueSv":""},"content":{"valueFi":"Lectures once a week, programming based hands-on exercises once a week, independent course project work, independent study","valueEn":"Lectures once a week, programming based hands-on exercises once a week, independent course project work, independent study","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"Materials in Moodle and on course Github","valueEn":"Materials in Moodle and on course Github","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"1.  ","valueEn":"1.  ","valueSv":"1.  "}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"The course assumes prior knowledge of language technology, basic understanding of neural networks, and fluency in Python. The course Introduction to Human Language Technology is a strongly recommended prerequisite and the course Introduction to Deep Learning is recommended.","valueEn":"The course assumes prior knowledge of language technology, basic understanding of neural networks, and fluency in Python. The course Introduction to Human Language Technology is a strongly recommended prerequisite and the course Introduction to Deep Learning is recommended.","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"0-5","valueEn":"0-5","valueSv":"0-5"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"Ability to complete the project, understanding of basic concepts in the application of deep neural networks to human language","valueEn":"Ability to complete the project, understanding of basic concepts in the application of deep neural networks to human language","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Assessment criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Assessment criteria 3","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 4","valueEn":"Assessment criteria 4","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kielet","valueEn":"Languages","valueSv":""},"content":{"valueFi":"englanti","valueEn":"English","valueSv":"engelska"}},{"title":{"valueFi":"Taso","valueEn":"Level","valueSv":""},"content":{"valueFi":"Syventävät opinnot","valueEn":"Advanced Studies","valueSv":""}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Tietojenkäsittelytieteet","valueEn":"Computer Science","valueSv":"Computer Science"}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Filip Ginter","valueEn":"Filip Ginter","valueSv":"Filip Ginter"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}