Course data
Course name: Artificial Intelligence in Data Science
Neptun ID: BMETE15MF75
Responsible teacher: János Török
Department: Department of Theoretical Physics
Programme: Courses for Physicist MSc students
Course data sheet: BMETE15MF75
Requirements, Informations



  • The course will be held traditionally in person. The first course on 9th of September will also be given. Since this is only an introductory course and last year I recorded all lectures those who cannot attend this lecture in person can watch the corresponding video.
  • Please note that the course relies on the fact that everybody has good knowledge of python especially numpy.
  • I will use four different systems to communicate with you (sorry):
    • neptun: most official, final notes, important and prompt messages
    • moodle: submit everything here, evaluation will also take place here. For final grade use the formula below and not the one moodle supplies.
    • the official institute webpage, requirements are posted here (may change due to virus lock down). Lectures and homeworks are posted here.
    • Microsoft meet: I will try to record the lectures and post it here. All online consultation will take place here if necessary. Teams links will be sent by a neptun message.
    • Please note, that I do not use microsoft teams on a daily basis, so please, do not use the chat system of the teams to communicate with me. If you have any question write an email to me torok.janos (at)



  • The faculty moodle system is located at You can log in using your university id. Courses should appear soon. All works must be submitted using the moodle system.
  • You are supposed to make pairs, homeworks are individual work, but the practice work, data challenge and the final projects are supposed to be group works.

Subjects covered


Introduction to machine learning from a physicist's perspective, with the aim to understand how it works and less emphasis on tricks or parameter oprimization


  1. Image segmentation
  2. Decision tree
  3. Deep learning (from scratch in numpy)
  4. Higher level implementations (tensorflow, sklearn, keras)
  5. Convolutional neural networks
  6. Pre-trained models
  7. Data augmentation
  8. Textual data
  9. Sequential data
  10. Game models


  • 50% homework
  • Project presented and accepted


  • Homeworks (individual): 100 points/HW
  • Practice solutions (pair): occasional varying number of points (extra points!)
  • Data challenge (pair): 500 points
  • Project and its presentation (pair): 1000 points
  • Marks:
    1. -1099
    2. 1100-1399
    3. 1400-1699
    4. 1700-1999
    5. 2000-


  • In moodle


  1. In the moodle


  1. ea01.pdf
  2. ea02.pdf
  3. ea03.pdf
  4. ea04.pdf
  5. ea05.pdf
  6. ea06.pdf
  7. ea07.pdf
  8. ea08.pdf
  9. ea09.pdf
  10. ea10.pdf

Old materials

Last modified: 02.09.2021