Course: null

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Course title -
Course code KMA/DEEP
Organizational form of instruction Lecture + Exercise
Level of course Master
Year of study not specified
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)
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