**Important note:**I am away until at least September 27, 2021. The first lecture will take place online, using your beloved MS Teams. The second lecture will be completely offline due to my travel. Moreover, I currently cannot predict the development of COVID-related travel measures in Czech Republic, which may require me to stay quaranteened after my return to the Czech Republic. Should this happen, the third lecture will be probably online again.

## Mathematical Tools for ITS

**Instructor:** Dr.-techn. Ing. Jan Přikryl <prikryl@fd.cvut.cz>

**Lectures:** Monday 09:45-11:15 K410 (group 26), Monday 13:15-14:45 K410 (group 25)

**Computer sessions:** Monday 11:30-13:00 K104 (group 26), Monday 15:00-16:30 K104 (group 25)

### Course description

The objective of this course is to establish the mathematical foundations necessary to understand general constructions useful for signal processing using Fourier Transform and the Short Time Fourier Transform, update and extend the knowledge about data processing and to provide an introduction to numerical methods for solving traffic flow equations. The course includes experimental and computer projects involving individual effort.

**Prerequisites:** Linear Algebra, Statistical Learning, MATLAB

### Texts

M. Vetterli, J. Kovacevic, and V. K. Goyal. “Fourier and Wavelet Signal Processing”. With permission of authors the preprint is available from our webpage as PDF here.

Broughton, S. A., & Bryan, K. (2018). *Discrete Fourier analysis and wavelets: applications to signal and image processing*. 2nd edition. John Wiley & Sons.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). *An introduction to statistical learning*. New York: Springer. Electronic version, errata, and supplementary material available from https://www.statlearning.com/.

Friedman, J., Hastie, T., & Tibshirani, R. (2009). *The elements of statistical learning.* Springer Series in Statistics. 2nd edition. New York: Springer. Electronic version, errata, and supplementary material available from https://web.stanford.edu/~hastie/ElemStatLearn/.

Heath, M. T. (2018). *Scientific Computing: An Introductory Survey, Revised Second Edition*. Society for Industrial and Applied Mathematics.

Li, J., & Chen, Y. T. (2019). *Computational partial differential equations using MATLAB®*. 2nd edition. CRC press.

### Grading

The following table lists all possible scores that can be obtained:

Homeworks | 21 points |

- minimum homework points |
9 points |

Final project |
14 points |

- minimum from the project |
7 points |

Total |
30(+5) points |

Final grade is given by the total points obtained for homeworks and the final project. The grading scheme is as follows:

Total points | Grade | ECTS |
---|---|---|

27 to 30 | excellent | A |

24 to <27 | very good | B |

21 to <24 | good | C |

18 to <21 | satisfactory | D |

16 to <18 | sufficient | E |

less than 16 | failed | F |