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Course title -
Course code KMA/DEEP
Organizational form of instruction Lecture + Exercise
Level of course Master
Year of study 1
Semester Winter
Number of ECTS credits 5
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)
  • Pavlačka Ondřej, RNDr. Ph.D.
Course content
unspecified

Learning activities and teaching methods
Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming)
Learning outcomes
Prerequisites
unspecified

Assessment methods and criteria
unspecified
Recommended literature
  • Bishop, Ch.M., Bishop, H. (2024). Deep Learning: Foundations and Concepts.
  • Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Sebastopol.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
  • Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
  • Higham, C. F., Higham, D. J. (2019). Deep Learning: An Introduction for Applied Mathematicians.
  • Chollet, F. (2021). Deep Learning with Python.
  • Murphy, K. P. (2023). Probabilistic Machine Learning: Advanced Topics.
  • Prince, S. J. (2023). Understanding Deep Learning.
  • Sutton, R. S., Barto, A. G. (2018). Reinforcement Learning: An Introduction.
  • Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into Deep Learning.


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): Mathematics (2026) Category: Mathematics courses 1 Recommended year of study:1, Recommended semester: Winter