{"id":null,"code":"521289S","name":{"valueFi":"Koneoppiminen","valueEn":"Machine Learning","valueSv":""},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[{"code":"opinfi-teema-new_technologies","title":{"valueFi":"Uudet teknologiat","valueEn":"New technologies","valueSv":"Nya teknologier"}},{"code":"opinfi_fitech","title":{"valueFi":"opinfi_fitech","valueEn":"opinfi_fitech","valueSv":"opinfi_fitech"}}],"createdAt":1790534854191,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"After completing the course, student\r\n1. can design and implement basic machine learning algorithms for regression and classification applications.\r\n2. can design and implement methods for optimizing cost functions for machine learning tasks.\r\n3. can apply the most common methods for machine learning.\r\n\r\nIn addition, the student learns about the following generic skills:\r\n4. is able to apply analytical and critical thinking skills in a manner appropriate to their discipline, taking into account the interfaces between fields and new knowledge\r\n5. is able to apply creative thinking and problem solving in their work in order to develop new knowledge and new methods.","valueEn":"After completing the course, student\r\n1. can design and implement basic machine learning algorithms for regression and classification applications.\r\n2. can design and implement methods for optimizing cost functions for machine learning tasks.\r\n3. can apply the most common methods for machine learning.\r\n\r\nIn addition, the student learns about the following generic skills:\r\n4. is able to apply analytical and critical thinking skills in a manner appropriate to their discipline, taking into account the interfaces between fields and new knowledge\r\n5. is able to apply creative thinking and problem solving in their work in order to develop new knowledge and new methods.","valueSv":""}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"The course covers machine learning fundamentals and key linear and nonlinear methods for regression and classification. You’ll learn to build models with good performance and strong generalization. Teaching includes theory lectures and self-paced, auto-graded programming exercises. Grades are based on completed tasks, with additional exercises available for interested students.\r\n\r\nContent: Introduction. Mathematical optimization for machine learning. Linear and non-linear models for regression and classification. Feature engineering and optimization. Model validation. Kernel methods, neural networks, tree-based learners.","valueEn":"The course covers machine learning fundamentals and key linear and nonlinear methods for regression and classification. You’ll learn to build models with good performance and strong generalization. Teaching includes theory lectures and self-paced, auto-graded programming exercises. Grades are based on completed tasks, with additional exercises available for interested students.\r\n\r\nContent: Introduction. Mathematical optimization for machine learning. Linear and non-linear models for regression and classification. Feature engineering and optimization. Model validation. Kernel methods, neural networks, tree-based learners.","valueSv":""}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study Methods","valueSv":""},"content":{"valueFi":"The course includes weekly programming assignments during eight weeks. It is compulsory to pass the first week's assignment and at least one task out of five in the following five weeks. Passing more tasks increases the course grade.","valueEn":"The course includes weekly programming assignments during eight weeks. It is compulsory to pass the first week's assignment and at least one task out of five in the following five weeks. Passing more tasks increases the course grade.","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Teaching Methods","valueSv":""},"content":{"valueFi":"Lectures 16 h, Fully guided working on assignments 4 h, Independent working on assignments 100 h (partially guided by assistants), and Self-study (text book and tutorial material for the Matlab environment) 15 h. \r\nCourse material is in Moodle learning environment. The programming tasks are done in Matlab language on MathWorks Grader online platform.","valueEn":"Lectures 16 h, Fully guided working on assignments 4 h, Independent working on assignments 100 h (partially guided by assistants), and Self-study (text book and tutorial material for the Matlab environment) 15 h. \r\nCourse material is in Moodle learning environment. The programming tasks are done in Matlab language on MathWorks Grader online platform.","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"<p>Text book. Instructions for the weekly laboratory works including supplementary links to supporting materials. Tutorials for MATLAB and MathWorks Grader system.</p>","valueEn":"<p>Text book. Instructions for the weekly laboratory works including supplementary links to supporting materials. Tutorials for MATLAB and MathWorks Grader system.</p>","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"1. Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos. Machine Learning Refined (Foundations, Algorithms, and Applications), 2nd edition, Cambridge University Press, 2020. ","valueEn":"1. Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos. Machine Learning Refined (Foundations, Algorithms, and Applications), 2nd edition, Cambridge University Press, 2020. ","valueSv":"1. Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos. Machine Learning Refined (Foundations, Algorithms, and Applications), 2nd edition, Cambridge University Press, 2020. "}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"The mathematics studies of the bachelor degree program of computer science and engineering, or equivalent. Especially knowledge of basic probability theory, matrix algebra and programming in MATLAB.","valueEn":"The mathematics studies of the bachelor degree program of computer science and engineering, or equivalent. Especially knowledge of basic probability theory, matrix algebra and programming in MATLAB.","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":"The course lasts eight weeks. The student follows weekly instructions for laboratory assignments and solves the presented programming problems (five in each week) independently on the The course lasts eight weeks. The student follows weekly instructions for laboratory assignments and solves the presented programming problems (five in each week) independently on the MathWorks Grader online system. The Grader system verifies the completed programming problems and provides basic hints for solving them. The number of correctly solved programming problems is used to grade the course. \r\nIt is compulsory to pass the first week's assignment and at least one programming problem out of five in the following five weeks. Passing more programming problems increases the course grade.\r\n\r\nThe course utilizes a numerical grading scale 1-5. In the numerical scale zero stands for a fail. \r\nRead more about assessment criteria at the University of Oulu webpage.","valueEn":"The course lasts eight weeks. The student follows weekly instructions for laboratory assignments and solves the presented programming problems (five in each week) independently on the The course lasts eight weeks. The student follows weekly instructions for laboratory assignments and solves the presented programming problems (five in each week) independently on the MathWorks Grader online system. The Grader system verifies the completed programming problems and provides basic hints for solving them. The number of correctly solved programming problems is used to grade the course. \r\nIt is compulsory to pass the first week's assignment and at least one programming problem out of five in the following five weeks. Passing more programming problems increases the course grade.\r\n\r\nThe course utilizes a numerical grading scale 1-5. In the numerical scale zero stands for a fail. \r\nRead more about assessment criteria at the University of Oulu webpage.","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":"Syventävät opinnot","valueEn":"Advanced Studies","valueSv":"Syventävät opinnot"}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Lääketieteen tekniikka (tietotekniikka), Tietotekniikka","valueEn":"Biomedical Engineering (Computer Science and Engineering), Computer Science and Engineering","valueSv":", "}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Matti Matilainen","valueEn":"Matti Matilainen","valueSv":"Matti Matilainen"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"Uudet teknologiat, opinfi_fitech","valueEn":"New technologies, opinfi_fitech","valueSv":"Nya teknologier, opinfi_fitech"}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}