COURSE SYLLABUS Advanced Data Mining 7.5 credits Avancerad Data Mining Second cycle, D7043E Version Autumn 2019 Sp 1 - Spring 2020 Sp 4Autumn 2020 Sp 1 - Present Course syllabus valid: Autumn 2020 Sp 1 - PresentThe version indicates the term and period for which this course syllabus is valid. The most recent version of the course syllabus is shown first. Education level Second cycle Grade scale U G VG * Subject Computer Science and Engineering Subject group (SCB) Computer Technology Main field of study Information Systems Sciences Entry requirementsIn order to meet the general entry requirements for the course, you must have accomplished a minimum of 120 ECTS of university studies, out of which 60 ECTS in the areas of computer or system science. The studies shall have included Introductory Programming (for example D0009E or D0007N) and Fundamentals of Databases (for example D0004N or D0018E) . The Advanced Data Mining Course also requires the completion of a basic data mining course such as D0025E. Good knowledge in English, equivalent to English 6. More information about English language requirements SelectionThe selection is based on 20-285 creditsCourse AimThe objective of the course is for the student to develop their knowledge and skills in Advanced Data Mining. After passing the course, the student should be able to: [1]. Use the advanced data mining concepts & techniques [2]. Explain how those concepts and techniques work [3]. Explain how concepts and techniques are, or should be, used in organizations [4]. Evaluate results of applying the concepts and techniques discussed above [5]. Analyze and reflect on the relationship between the techniques, the dataset, the problem or opportunity in hand, and the tools and technology used ContentsThe course is an advanced course in data mining. The course provides knowledge to address various data science problems and datasets. Focus lies on advanced machine learning techniques for classification, regression, clustering, and anomaly detection, for example decision trees, random forests, neural networks, including Support Vector Machines and Deep Learning, Expectation Maximization (EM), Markov models, and Bayesian networks.RealizationLectures, labs, assignments, case studies and project work. Laboratory work requires access to very high computational capacity. During the course, the students work in small groups. Some assignments or case studies in the course might contain work in contact with or about the industry. The student uses different methods and techniques, and it is important to choose the right method, technique or computer support for each task. Before and after the tasks are solved, there are lectures to present and discuss different solutions. ExaminationThrough individual tests and group/project assignment, different student abilities are examined. Those are: the ability to explain and use advanced data mining techniques and the ability to solve business problems using data mining individually and in groups.RemarksTechnical Requirements: access to PC with Windows XP, microphone, Web cam and permission to install software. Internet connection (minimum 0,5 Mbps).ExaminerMarcus LiwickiLiterature. Valid from Autumn 2019 Sp 1 (May change until 10 weeks before course start)Pang-Ning Tan, Michael Steinbach, and Vipin Kumar: Introduction to Data Mining, Addison Wesley, 2005. Search books in the library » Course offered byDepartment of Computer Science, Electrical and Space EngineeringModules CodeDescriptionGrade scaleHPStatusFrom periodTitle 0001Individual examU G VG4.00MandatoryA19 0002Individual tasksU G#1.50MandatoryA19 0003Group/Project workU G#2.00MandatoryA19 Study guidanceStudy guidance for the course is to be found in our learning platform Canvas before the course starts. Students applying for single subject courses get more information in the Welcome letter. You will find the learning platform via My LTU.Syllabus establishedby Jonny Johansson, HUL SRT 21 Nov 2018Last revisedby Director of Undergraduate Studies Jonny Johansson, Department of Computer Science, Electrical and Space Engineering 06 Feb 2020