IB031 Introduction to Machine Learning

Faculty of Informatics
Spring 2025
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
In-person direct teaching
Teacher(s)
doc. RNDr. Tomáš Brázdil, Ph.D. (lecturer)
RNDr. Jaroslav Čechák, Ph.D. (seminar tutor)
Mgr. Monika Čechová, Ph.D. (seminar tutor)
Mgr. Tomáš Foltýnek, Ph.D. (seminar tutor)
Bc. Filip Gregora (seminar tutor)
Ing. Bc. Michaela Kecskésová (seminar tutor)
doc. Mgr. Bc. Vít Nováček, PhD (seminar tutor)
Bc. Tomáš Pavlík (seminar tutor)
Mgr. et Mgr. Bc. Pavla Wernerová (assistant)
Guaranteed by
doc. RNDr. Tomáš Brázdil, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Timetable
Fri 21. 2. to Fri 16. 5. Fri 8:00–9:50 C33
  • Timetable of Seminar Groups:
IB031/01: Tue 18. 2. to Tue 13. 5. Tue 8:00–9:50 C121, T. Foltýnek
IB031/02: Mon 17. 2. to Mon 12. 5. Mon 18:00–19:50 C121, J. Čechák
IB031/03: Mon 17. 2. to Mon 12. 5. Mon 14:00–15:50 C121, M. Čechová
IB031/04: Wed 19. 2. to Wed 14. 5. Wed 18:00–19:50 C122, M. Kecskésová
IB031/05: Wed 19. 2. to Wed 14. 5. Wed 14:00–15:50 C121, F. Gregora
IB031/06: Tue 18. 2. to Tue 13. 5. Tue 14:00–15:50 C122, T. Pavlík
IB031/07: Mon 17. 2. to Mon 12. 5. Mon 16:00–17:50 C121, J. Čechák
IB031/08: Wed 19. 2. to Wed 14. 5. Wed 12:00–13:50 C122, V. Nováček
IB031/09: Mon 17. 2. to Mon 12. 5. Mon 8:00–9:50 C122, F. Gregora
Prerequisites
Recommended courses are MB152 a MB153.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 40 fields of study the course is directly associated with, display
Course objectives
By the end of the course, students should know basic methods of machine learning and understand their basic theoretical properties, implementation details, and key practical applications. Also, students should understand the relationship among machine learning and other sub-areas of mathematics and computer science such as linear algebra, statistics, artificial intelligence and optimization.
Learning outcomes
By the end of the course, students
- will know basic methods of machine learning;
- will understand their basic theoretical properties, implementation details, and key practical applications;
- will understand the relationship among machine learning and other sub-areas of mathematics and computer science such as linear algebra, statistics, artificial intelligence, and optimization;
- will be able to implement and validate a simple machine learning method.
Syllabus
  • Basic machine learning: classification and regression, clustering, (un)supervised learning, simple examples
  • Decision trees: learning of decision trees
  • Evaluation: training and test sets, overfitting, confusion matrix, learning curve, ROC curve
  • Probabilistic models: Bayes rule, naive Bayes; introduction to Bayes networks
  • Linear regression (classification): least squares, relationship wih MLE, regression trees
  • Kernel methods: SVM, kernel transformation, kernel trick
  • Neural networks: multilayer perceptron, backpropagation, non-linear regression
  • Lazy learning: nearest neighbor method; Clustering: k-means, hierarchical clustering
  • Practical machine learning: Data pre-processing: attribute selection and construction, sampling. Ensemble methods. Bagging. Boosting. Tools for machine learning.
Literature
    recommended literature
  • MITCHELL, Tom M. Machine learning. Boston: McGraw-Hill, 1997, xv, 414. ISBN 0070428077. info
    not specified
  • GÉRON, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems. Second edition. Beijing: O'Reilly, 2019, xxv, 819. ISBN 9781492032649. info
  • ROGERS, Simon and Mark GIROLAMI. A first course in machine learning. Boca Raton: CRC Press/Taylor & Francis Group, 2012, xx, 285. ISBN 9781439824146. info
  • FLACH, Peter A. Machine learning : the art and science of algorithms that make sense of data. New York: Cambridge University Press, 2012, xvii, 396. ISBN 1107422221. info
  • Pattern recognition and machine learning. Edited by Christopher M. Bishop. New York: Springer, 2006, xx, 738. ISBN 0387310738. info
  • BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Bookmarks
https://is.muni.cz/ln/tag/FI:IB031!
Teaching methods
Lectures + practical exercises + project
Assessment methods
Intrasemestral exam, project, final exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.
  • Enrolment Statistics (recent)
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