- First lesson in term SS 20: Tuesday, 21.04.2020
Important notice for the summer term 2020: The course will initially be offered as a synchronous distance learning course during the SARS-CoV-2-related restrictions in term SS 20. I will post the corresponding Zoom link to the group of online registered users in the Persönlicher Stundenplan before 20.04.2020. The timetable according to the Starplan will apply. If or when we may meet again in lecture halls, the room indicated in Starplan will apply.
For saving Zoom-Breakoutrooms in a persistent way, it is necessary that you register for a free account at Zoom. Then insert the email-address of your Zoom-Account into the second column of this group-assignment-spreedsheat.
Data Mining Lab: Contents
In this course 6 different data mining and pattern recognition applications are implemented by all student groups. A group contains at most 3 students. The implementation of each application should be done within one session. The applications, which have to be implemented, are described in the subsections below.
For each of the 6 lab excercises:
- a jupyter-notebook is provided, which contains the task-description and questions.
- students have to prepare themselves before the exercise-date. For a focused preparation a list of preparation questions is contained in the jupyter-notebook of each exercise. These questions will be interrogated randomly at the start of each excercise.
- the tasks as formulated in the jupyter-notebook must be implemented in the code-cells. Moreover, the questions must be answered in the jupyter-notebook.
- Important: Even though it is not always explicitly stated, the obtained results must be discussed scientifically: Try to explain the results, document what you find interesting, propose improvements, …This discussion must also be included in the jupyter-notebook.
- the prepared jupyter-notebooks (as described in the previous items, including the answers on the preparation questions!) must be submitted to the lecturer. Due date for each notebook, is immediately before the start of the next lab-exercise. The Jupyter Notebook (.ipynb), it’s .html representations and a link to download the entire project must be submitted.
- Each exercise is marked. The final mark is the average over all 6 marks.
- Unexcused absence yields a submark of 4.7.
Global Health Data Analysis:
In this exercise data on global health and nutrition is analysed. In particular
- life-expectancy per country is visualized in a global map
- correlations between nutrition-facts, such as daily calories consumption per capita, and life-expectancy is analysed
- machine-learning models to predict life-expectancy from nutrition features are trained
- countries are clustered according to their nutrition-development within the last 50 years
For this exercise two sessions are allocated.
Recommender Systems are applied in E-commerce for generating customized recommendations. Well known are the Amazon.com recommendations which are either distributed by e-mail or presented on the Amazon web page after login. For generating these recommendations the products which have already purchased or reviewed by the user are taken into account. In this exercise the currently most popular algorithms (Collaborative Filtering) for generating recommendations are implemented, tested and analysed.
Clustering of music files and automatic playlist generation:
In this exercise a collection of mp3 encoded music files is first transcoded to the .wav format. From the .wav files a comprehensive set of audio features ise extracted. The corresponding feature-vectors are then clustered, such that the clusters contain similar music-files.
A Naive Bayes Classifier is implemented for filtering spam. It is also shown how to apply this algorithm for document classification in general
In this excercise a programm for face recognition is implemented. For a given set of training images (biometrical face photos) the Principal Component Analysis (PCA) is applied to calculate the space of eigenfaces. Then a photo which has to be recognized is transformed to the space of eigenfaces and the closest training photo is calculated.
Traffic Sign Recognition with Deep Neural Networks:
In this excercise a Convolutional Neural Network (CNN) for the recognition of German traffic signs must be implemented, using tensorflow and keras.
Dates and Documents
The links in the table below refer to the exercise-instruction-notebooks. However, for interactively working with the notebooks, they must be downloaded. All notebooks and resources can be downloaded from GitLab project. For executing jupyter-notebooks, Python and jupyter-notebooks must be installed. It is strongly recommended to install the Anaconda Python distribution. This distribution does not only contain Python and Jupyter-Notebooks but also nearly all packages, which are required in this lab-exercise. See the Tipps&Tricks notebook in the Gitlab repo of this course for further hints on the setup of the development environment.
|21.04.2020||Introduction, Organizational aspects|
|28.04.2020||Registration, Python Introduction, Environment Setup||Data Science Programming Course|
|05.05.2020||Global Health Data||Global Health Data (.ipynb)|
|12.05.2020||Global Health Data|
|19.05.2020||Collaborative Recommender Systems||Recommender Systems(.ipynb)|
|26.05.2020||Collaborative Recommender Systems|
|09.06.2020||Music Clustering||Music Clustering (.ipynb)|
|16.06.2020||Document Classification||Document Classification (.ipynb)|
|23.06.2020||Face Recognition||Face Recognition (.ipynb)|
|30.06.2020||Traffic Sign Recognition, Convolutional Neural Networks (CNNs)||Traffic Sign Classification (.ipynb)|