{"id":null,"code":"521495A","name":{"valueFi":"Tekoäly","valueEn":"Artificial Intelligence","valueSv":""},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[{"code":"opinfi_fitech","title":{"valueFi":"opinfi_fitech","valueEn":"opinfi_fitech","valueSv":"opinfi_fitech"}}],"createdAt":1790534825448,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"Kurssin suorittamisen jälkeen opiskelija\r\n\r\n- tuntee ongelmanratkaisuun ja optimointiin soveltuvien hakumenetelmien perusteet;\r\n- ymmärtää, miten hakuun perustuvia päätöksiä tehdään pelityyppisissä sovelluksissa;\r\n- tietää todennäköisyyksiin perustuva päättelyn periaatteet tekoälyjärjestelmissä;\r\n- tuntee, miten rationaalinen päätöksenteko epävarmuuden vallitessa muotoillaan hyötyteorian avulla;\r\n- ymmärtää koneoppimisen perusteet ja miten joitakin vakiintuneita menetelmiä voidaan soveltaa tekoälyongelmiin;\r\n- tuntee edistyneet tekoälyn sovellukset\r\n\r\nKurssiprojekteissa opiskelijat saavat kokemusta ohjelmoinnista sekä haku-, päätöksenteko- ja koneoppimismenetelmien käytöstä.","valueEn":"After completing the course, students\r\n- know the basic search strategies that can be applied in problem solving and optimization.\r\n- understand how search-based decisions are made in game-like competitive applications.\r\n- know the basic principles of probabilistic reasoning in artificial intelligence systems.\r\n- know how rational decision making under uncertainty can be formulated using utility theory.\r\n- understand the fundamentals of machine learning and how some of the established methods can be applied to problems in AI.\r\n- are familiar with advanced AI applications\r\n\r\nIn the course projects, students get some experience in programming and using search, decision making and machine learning methods.","valueSv":""}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"älykkäiden agenttien tyypit, ei-informoitu ja informoitu (heuristinen) haku, paikallinen haku, rajoitelaskenta, haku peliongelmissa, epävarmuuden käsittely, todennäköisyyslaskentaan perustuva päättely, koneoppiminen, utiliteetti, päätösverkot, Markovin päätösprosessi, vahvistusoppiminen, tekoälyn sovellukset","valueEn":"Intelligent agent types, uninformed search methods, informed (heuristic) search, local search, constraint satisfaction problems, adversarial search, uncertainty handling, probabilistic reasoning, utility, machine learning, decision networks, Markov decision process, reinforcement learning, applications of artificial intelligence","valueSv":""}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study Methods","valueSv":""},"content":{"valueFi":"Kurssin suoritukseen sisältyy luentojen esitehtäviä, viikoittaisia laskuharjoitus- ja ohjelmointitehtäviä, jotka arvioidaan, sekä loppukoe.","valueEn":"Course consists of preliminary tasks for lectures, weekly math exercises and programming assignments, which are graded, and a final exam.","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Teaching Methods","valueSv":""},"content":{"valueFi":"Opetusmuoto: hybridi\r\nLuennot 28h ja harjoitukset 12h kontaktiopetuksena (luentoja voi seurata etänä), esitehtävät, harjoitustehtävät ja itseopiskelu 95h\r\nLopputentti järjestetään salitenttinä opetusperiodin lopussa, uusintatentit keväällä ja syksyllä","valueEn":"Teaching mode: hybrid\r\nLectures 28h and exercises 12h as contact teaching (lectures can be followed remotely), preliminary tasks, exercise tasks and self-study 95h\r\nFinal exam is in class at the end of teaching period, retake exams in Spring and Autumn","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"<p>Luentokalvot (v&auml;litet&auml;&auml;n Moodlen kautta), kurssikirja (e-kirja saatavana yliopiston kirjaston kautta).</p>","valueEn":"<p>Lecture slides (delivered via Moodle), course book (e-book available via Oulu University library).</p>","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"1. S. Russell, P. Norvig: Artificial Intelligence - A Modern Approach. 4th Edition (global). Chapters 1-6, 12-16, 19, 21-23, partly 24-27 978-1-292-40113-3","valueEn":"1. S. Russell, P. Norvig: Artificial Intelligence - A Modern Approach. 4th Edition (global). Chapters 1-6, 12-16, 19, 21-23, partly 24-27 978-1-292-40113-3","valueSv":"1. S. Russell, P. Norvig: Artificial Intelligence - A Modern Approach. 4th Edition (global). Chapters 1-6, 12-16, 19, 21-23, partly 24-27 978-1-292-40113-3"}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"Kurssin \"521160P Johdatus tekoälyyn\" suorittamista suositellaan, mutta se ei ole välttämätön. Lisäksi suositellaan, että opiskelija tuntee tilastomatematiikan perusteita (esim. kurssi 031021P \"Tilastomatematiikka\") ja tietää Python-ohjelmoinnin perusteet (esim. kurssi 521141P \"Ohjelmoinnin alkeet\").","valueEn":"Completion of the course \"521160P Introduction to Artificial Intelligence\" is recommended, but is not a prerequisite. It is also recommended that a student has done studies related to probability and statistics (e.g., course \"031021P Probability and Mathematical Statistics\") and Python programming (e.g., course \"521141P Elementary Programming\").","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"1-5/HYL","valueEn":"1-5/FAIL","valueSv":"1-5/FAIL"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"Arviointi perustuu kurssin aikana suoritettuihin tehtäviin ja loppukokeeseen.","valueEn":"The assessment of the course is based on the tasks done during the course and the final exam.","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Evaluation criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Evaluation criteria 3","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 4","valueEn":"Evaluation criteria 4","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Pääasiallinen opetuskieli","valueEn":"Primary Teaching Language","valueSv":""},"content":{"valueFi":"englanti","valueEn":"English","valueSv":"engelska"}},{"title":{"valueFi":"Taso","valueEn":"Level","valueSv":""},"content":{"valueFi":"Aineopinnot","valueEn":"Intermediate Studies","valueSv":""}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Tietotekniikka","valueEn":"Computer Science and Engineering","valueSv":""}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Pekka Sangi, Jaakko Suutala","valueEn":"Pekka Sangi, Jaakko Suutala","valueSv":"Pekka Sangi, Jaakko Suutala"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"opinfi_fitech","valueEn":"opinfi_fitech","valueSv":"opinfi_fitech"}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}