Course title: DATA MINING
Semester: Winter
Lectures/Classes: 30 / 30 hours
Field of study: Bioinformatics
Study cycle: 2nd cycle
Type of course: optional
Contact person: dr Jacek Lewandowski
Short description: The module introduces the process of data mining, machine learning, predictive modeling, data classification, data clustering, analysis of association rules and time series.
Full description: The module will present the application of basic techniques of "data, mining" (predictive modeling, data grouping, analysis of association rules, time series) in scientific problems as well as in the business context. As part of practical classes, algorithms in the field of machine learning, such as classification and data classification algorithms as well as statistical algorithms most often used in the data mining process, will be discussed and used in practice
Bibliography: 1. Witten I.H., Frank E., Hall M.A., Pal C.J.: Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, Morgan Kaufman 2016 2. Han J., Kamber M.: Data Mining: Concepts and Techniques, Third Edition, Elsevier 2012 3. Provost F., Fawcett T.: Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, O'Reilly 2013 4. Hastie T., Tibshirani R., Friedman J. H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer 2009 5. Hand D.J., Mannila H., Smyth P.: Principles of Data Mining, A Bradford Book 2001
Learning outcomes: Knowledge: The student has the knowledge of advanced data mining methods The student knows the application of the most important data mining methods to scientific and business problems in the tasks of data analysis of large dimensions - methods of predictive modeling, data grouping, classification and generation of association rules. The student knows the principles of the most important computational algorithms used in data mining. Skills: The student can choose the right methods / algorithms for the given data mining task. The student can accomplish the task of predictive modeling in the selected data mining tool. Social competences: The student is able to independently expand knowledge and skills in the field of developed methods and tools for data mining.
Assessment methods and assessment criteria: PC Lab assesment Written reports on the set of data mining exercises performed in lab. Student will be assessed on their in-class performance based on random oral tests with the lecturer on progress in learning and activity. Presence in class is obligatory; the student may have only one unjustified absence. In case of an excused absence, the student is required to pass the relevant part of the material. The PC lab mark will be based on the average reports mark. Module assesment The module mark is based on the written test, taken by students who has completed and passed the PC lab.

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