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The exercise courses take place on:

Monday 14:15 - 15:00 (00.151-113)

Thursday 10:15 - 11:00 (02.133-113)

Both courses cover the same topics. If there are any questions regarding the exercises that could not be clarified within the courses, feel free to write to Opens window for sending emailDaniel Stromer.


- The exercises will start in the second week of the semester (23.10/26.10)

Welcome to the Exercises for Introduction to Pattern Recognition!

The topics are practical tasks that are closely related to the lecture. Please keep in mind that the exercise will be part of the exam.

Both exercise sessions cover the same content. For all exercises, Opens external link in new windowJupiter Notebooks will be provided where you have to fill in code snippets.

A single session will typically take about 45 minutes. Exercise sheets and their solutions will become available on this website.

Older lecture videos are available at Opens external link in new window (only accessible if you are inside the university network; if you want to watch the videos from home, consider to tunnel the connection). Be aware that the contents of the lecture have changed since then!

Some remarks about programming

We will shortly go over the Python code for the practical exercises in the sessions. The solutions will be provided (password-protected) after another week. The password will be announced in the next sessions, or by request (write me an Opens window for sending emailemail).


Exercise Date Notebook Hints
1 Introduction to Python   23.10. || 26.10.

Initiates file downloadExercise_1.ipynb

Initiates file downloadExercise_1.html

Initiates file downloadSolution_1.ipynb

Initiates file downloadSolution_1.html

2 Functions and Images30.10. || 02.11.

Initiates file downloadExercise_2.ipynb

Initiates file downloadExercise_2.html

Initiates file downloadlena.png

Initiates file downloadSolution_2.ipynb

Initiates file downloadSolution_2.html

NO EXERCISE (due to public holiday the week before)06.11. || 09.11.n.a.not provided
3 Fourier Transform and Sampling13.11. || 16.11.

Initiates file downloadExercise_3.ipynb

Initiates file downloadExercise_3.html

Initiates file downloadsample.wav

Initiates file downloadPart_Solution_3.ipynb
4 k-means clustering20.11. || 23.11.

Initiates file downloadExercise_4.ipynb

Initiates file downloadExercise_4.html

Initiates file downloadPart_Solution_4.ipynb
5 Histograms, Thresholding and Filtering27.11. || 30.11.

Initiates file downloadExercise_5.ipynb

Initiates file downloadExercise_5.html

Initiates file downloadletters.jpg

Initiates file downloadxray.jpg

Initiates file downloadPart_Solution_5.ipynb
6 Sharpening and Edge Detection04.12. || 07.12.

Initiates file downloadExercise_6.ipynb

Initiates file downloadExercise_6.html

Initiates file downloadmoon.jpg

Initiates file downloadPart_Solution_6.ipynb
7 Morphological Operations11.12. || 14.12.

Initiates file downloadExercise_7.ipynb

Initiates file downloadExercise_7.html

Initiates file

Initiates file downloadPart_Solution_7.ipynb
8 Discrete Fourier Transform18.12. || 21.12.

Initiates file downloadExercise_8.ipynb

Initiates file downloadExercise_8.html

already included

2-D Filtering in Fourier Domain 

Walsh-Hadamard Transform

08.01. || 11.01.

Initiates file downloadExercise_9.ipynb

Initiates file downloadExercise_9.html

already included
10 Wavelet Transforms for Image Compression15.01. || 18.01.

Initiates file downloadExercise_10.ipynb

Initiates file downloadExercise_10.html

Initiates file downloadwavelet_pipeline.jpg

already included
11 Eigenfaces22.01. || 25.01.

Initiates file downloadExercise_11.ipynb

Initiates file downloadExercise_11.html

Initiates file

already included
12 Refresher29.01. || 01.02.not provided
13 Classifiers05.02. || 08.02.

Initiates file downloadExercise_12.ipynb

Initiates file downloadExercise_12.html

not provided