Data Mining Lab

Organisation

Announcements

• First lesson in term WS 22-23: Thursday, 10.10.22
• This lecture is held by Johannes Theodoridis and Manuel Eberhardinger

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.

Vehicle Data Analysis:

This exercise applies a comprehensive collection of 25000 vehicles. Based on this dataset, we implement

• an Explorative Data Analysis (EDA) to understand the data
• an entire Machine Learning process, from data access to model evaluation
  
• a classifier to predict productgroup from input-features
• a regression-model to predict CO2-emissions from Input features
• Hyperparameter-Tuning

Recommender Systems:

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.

Genetic Algorithm for Image Generation

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.

Spam Filter:

A Naive Bayes Classifier is implemented for filtering spam. It is also shown how to apply this algorithm for document classification in general

Face Recognition:

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

All notebooks and resources can be downloaded from Ilias Data Mining. 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.

Literature

• Programming collective intelligence : building smart web 2.0 applications (23 August 2007) by Toby Segaran
• Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) (22 June 2005) by I. H. Witten, Eibe Frank
• Natural Language Processing with Python (2009) by Steven Bird, Ewan Klein, Edward Loper