A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.
MI-ADM.16 Data Mining Algorithms Extent of teaching: 2P+1C Instructor: Completion: Z,ZK Department: 18105 Credits: 5 Semester: L Annotation:
The course focuses on algorithms used in the fields of machine learning and data mining. However, this is not an introductory course, and the students should know machine learning basics. The emphasis is put on advanced algorithms (e.g., gradient boosting) and non-basic kinds of machine learning tasks (e.g., recommendation systems) and models (e.g., kernel methods).
Lecture syllabus:
1. Recalling basic data mining methods and their applications. 2. Model evaluation. 3. Bias-variance decomposition, negative correlation learning. 4. Decision trees and ensemble methods based on them. 5. - 6. (2) Boosting and gradient boosting (XGBoost). 7. Introduction to kernel methods. 8. Kernel methods. 9. Modern kernel methods. 10. - 11. (2) Introduction to recommendation systems, usage of kNN. 12. Matrix factorisation for reccomendation. 13. Hyperparameters tuning, AutoML, new trends. Seminar syllabus:
Literature:
[1] Hastie T.,Tibshirani R.,Friedman J., The Elements of Statistical Learning, Data Mining, Inference and Prediction, Springer, 2011 [2] Murphy, K. P., Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series), The MIT Press, 2012 [3] Aggarwal, Ch. C., Recommender Systems, Springer, 2016 Requirements:
Statistics, algorithmization, BIE-VZD - Introduction to data mining.
Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/MI-ADM/ The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester MI-WSI-WI.2016 Web and Software Engineering V 2 MI-WSI-ISM.2016 Web and Software Engineering PZ 2 MI-PSS.2016 Computer Systems and Networks V 2 MI-SP-TI.2016 System Programming V 2 MI-SP-SP.2016 System Programming V 2 MI-SPOL.2016 Unspecified Branch/Specialisation of Study VO 2 MI-PB.2016 Computer Security V 2 MI-WSI-SI.2016 Web and Software Engineering V 2 MI-NPVS.2016 Design and Programming of Embedded Systems V 2 MI-ZI.2016 Knowledge Engineering PO 2 MI-ZI.2018 Knowledge Engineering PO 2
Page updated 17. 4. 2024, semester: Z/2021-2, L/2019-20, Z/2020-1, Z/2022-3, Z/2019-20, L/2022-3, Z/2024-5, Z/2023-4, L/2021-2, L/2020-1, L/2023-4, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška