{"id":null,"code":"TKO_3120","name":{"valueFi":"Machine Learning and Pattern Recognition","valueEn":"Machine Learning and Pattern Recognition","valueSv":"Machine Learning and Pattern Recognition"},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[],"createdAt":1790534308236,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"The course enables to learn many classical machine learning and pattern recognition methods which can be used to build models and systems based on observed data. After the course students understand the main principles of machine learning and pattern recognition methods and steps needed for applying them in real applications. The students especially learn the core concepts of overfitting and underfitting and are able to find a suitable balance between these extremes in a given problem at hand.","valueEn":"The course enables to learn many classical machine learning and pattern recognition methods which can be used to build models and systems based on observed data. After the course students understand the main principles of machine learning and pattern recognition methods and steps needed for applying them in real applications. The students especially learn the core concepts of overfitting and underfitting and are able to find a suitable balance between these extremes in a given problem at hand.","valueSv":"The course enables to learn many classical machine learning and pattern recognition methods which can be used to build models and systems based on observed data. After the course students understand the main principles of machine learning and pattern recognition methods and steps needed for applying them in real applications. The students especially learn the core concepts of overfitting and underfitting and are able to find a suitable balance between these extremes in a given problem at hand."}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"This course covers the main theories, techniques, and algorithms in machine learning and pattern recognition, starting with simple topics such as linear regression/classification and ending up with more advanced topics such as artificial neural networks, ensemble learning, and decision trees. For pattern recognition most popular feature extraction and feature selection techniques are introduced and Bayesian decision theory is studied. Main classical machine learning and pattern recognition techniques are considered with emphasize on how, why and when they work.","valueEn":"This course covers the main theories, techniques, and algorithms in machine learning and pattern recognition, starting with simple topics such as linear regression/classification and ending up with more advanced topics such as artificial neural networks, ensemble learning, and decision trees. For pattern recognition most popular feature extraction and feature selection techniques are introduced and Bayesian decision theory is studied. Main classical machine learning and pattern recognition techniques are considered with emphasize on how, why and when they work.","valueSv":"This course covers the main theories, techniques, and algorithms in machine learning and pattern recognition, starting with simple topics such as linear regression/classification and ending up with more advanced topics such as artificial neural networks, ensemble learning, and decision trees. For pattern recognition most popular feature extraction and feature selection techniques are introduced and Bayesian decision theory is studied. Main classical machine learning and pattern recognition techniques are considered with emphasize on how, why and when they work."}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study methods","valueSv":""},"content":{"valueFi":"Written exam and exercise work","valueEn":"Written exam and exercise work","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Course unit methods","valueSv":""},"content":{"valueFi":"Lectures 28 h\r\n\r\nEstimate of a student workload:\r\n- lectures 28 h\r\n- exercise work 50 h\r\n- self-study 55 h\r\n- examination 2 h","valueEn":"Lectures 28 h\r\n\r\nEstimate of a student workload:\r\n- lectures 28 h\r\n- exercise work 50 h\r\n- self-study 55 h\r\n- examination 2 h","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"Theodoridis S. and Koutroumbas K., Pattern Recognition, 4th edition or later, Elsevier, 2009\r\nOther material will be announced during the lectures","valueEn":"Theodoridis S. and Koutroumbas K., Pattern Recognition, 4th edition or later, Elsevier, 2009\r\nOther material will be announced during the lectures","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"Good knowledge of Python programming is beneficial.","valueEn":"Good knowledge of Python programming is beneficial.","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"","valueEn":"","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":"","valueEn":"","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":"Jukka Heikkonen, Paavo Nevalainen","valueEn":"Jukka Heikkonen, Paavo Nevalainen","valueSv":"Jukka Heikkonen, Paavo Nevalainen"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}