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Course title -
Course code KMA/STROY
Organizational form of instruction Lecture + Exercise
Level of course Bachelor
Year of study 3
Semester Winter
Number of ECTS credits 6
Language of instruction Czech
Status of course Compulsory, Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Fürst Tomáš, RNDr. Ph.D.
  • Pavlačka Ondřej, RNDr. Ph.D.
Course content
unspecified

Learning activities and teaching methods
unspecified
Learning outcomes
Prerequisites
unspecified
KMA/MA1 and KAG/LA1A and KMA/BAY

Assessment methods and criteria
unspecified
Recommended literature
  • Bishop, Ch. M. (2011). Pattern recognition and machine learning.
  • Bishop, Ch.M., Bishop, H. (2024). Deep Learning: Foundations and Concepts.
  • Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. 2nd Ed. O´Reilly.
  • Hastie, T., Tibshirani, R., Friedman, J. (2016). The Elements of Statistical Learning.
  • Chollet, F. (2021). Deep Learning with Python. 2nd Edition. Manning.
  • Raschka, S., Mirjali, V. (2019). Python Machine Learning. Birmingham.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Science Study plan (Version): Applied Mathematics - Specialization in Business Mathematics (2026) Category: Mathematics courses 3 Recommended year of study:3, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Applied Mathematics - Specialization in Data Science (2026) Category: Mathematics courses 3 Recommended year of study:3, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Applied Mathematics - Specialization in Industrial Mathematics (2026) Category: Mathematics courses 3 Recommended year of study:3, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Applied Mathematics - Specialization in Mathematics for Sustainable Innovation (2026) Category: Mathematics courses 3 Recommended year of study:3, Recommended semester: Winter