About

My name is Eva Maria Kiss, and my teaching and research at the University of Applied Sciences Kaiserslautern focuses on database systems, machine learning, web technologies, also basic programming courses and algorithms. This website provides additional learning materials such as tutorials, quizzes and learning apps that can be used for self-study and fun.

Tutorials and Quizzes

On this website you find Tutorials and Quizzes intended to support self-study and exam preparations.

  • Python Cheatsheet »

    This Python Cheatsheet summarizes basic syntax and usage on how to get started with Python. It is organized in ten blocks, corresponding to the usual steps of learning of a programming language: prerequisites, installation and tools, how to create and run scripts and notebooks, how to use variables, perform calculations with operators, use if-else, loops, data structures, functions, classes, packages. To run the code examples, you can use your own Python installation with Jupyter Notebook, Visual Studio Code, Spyder ... or use one of the many available online Python environments (Google Colaboratory, Online GDB, ...).

    Used in Machine Learning projects
  • MATLAB Tutorial »

    The online MATLAB Tutorial from this website gives a brief introduction to the MATLAB language. Basic MATLAB syntax (variables, operators, input, output, arrays, matrices, functions, plotting) is illustrated using small examples that are saved as MATLAB scripts. The examples will also run in the open source software Octave. After completing this tutorial, you can take a MATLAB Quiz to test your MATLAB knowledge.

    Used in Master's module Numerical Methods
  • R Tutorial »

    This tutorial is an introduction to the statistical programming language R and covers the basic syntax: variables and data types, data structures (vectors, matrices, data frames), control flow, functions, data visualization and the most important packages / libraries. We use RStudio and RStudio Cloud as an integrated, user-friendly development environment for R.

    Used in Machine Learning projects
  • SQL-Tutorial »

    Das SQL-Tutorial gibt eine kompakte Übersicht der wichtigsten und am meisten genutzten SQL-Befehle, gruppiert nach Einsatzbereich: Befehle zur Datenabfrage und Datenmanipulation (DML, data manipulation language), Befehle zur Definition von Datenbankobjekten (DDL, data definition language), und Befehle zur Kontrolle von Zugangsberechtigungen (DCL, data control language). Die Beispiele beziehen sich auf eine University-Datenbank, die die Daten über Studiengänge, Module, Studenten und Prüfungen in einer Universitätsumgebung verwaltet, und die als Datenhaltungsschicht für eine Webanwendung verwendet werden soll.

    Used in Master's Module Database Systems
  • SQL-Kurzreferenz »

    Die SQL-Kurzreferenz gibt eine kompakte tabellarische Übersicht der wichtigsten SQL-Befehle. Die Beispiele beziehen sich auf die University-Datenbank aus dem SQL-Tutorial. Hinweise zur Syntax-Notation: Die SQL-Schlüsselwörter sind in Großbuchstaben geschrieben, z.B. SELECT, INSERT, UPDATE. Optionale Bestandteile ("Klauseln") eines SQL-Befehls werden in eckige Klammern gesetzt.

    Used in Master's Module Database Systems
  • Erste Schritte mit phpMyAdmin »

    Das vorliegende phpMyAdmin-Tutorial gibt eine kompakte Übersicht über die Verwendung von phpMyAdmin zur Erstellung, Verwaltung und Abfrage von MySQL- bzw. MariaDB-Datenbanken. Wir verwenden dabei das XAMPP-Programmpaket, das MySQL/MariaDB und phpMyAdmin beinhaltet, und zeigen, wie die Struktur einer Datenbank (Tabellen, Integritätseinschränkungen, Sichten) mit der Benutzeroberfläche erstellt wird, wie die Datenbank über die Benutzeroberfläche und mit der Import-Funktionalität mit Daten befüllt wird, und wie Datenbank-Struktur und Inhalte mit der Export-Funktionalität exportiert werden.

    Used in Master's Module Database Systems
  • Learning apps

    The following apps created with Python / Jupyter Notebook / Google Colab and R / Shiny can be used for learning about algorithms and data visualization. Some of the apps are work in progress, to be finished as project work by students. Have fun!

  • BSTLearner   Algorithms Binary Search Trees Python

    A binary search tree (BST) is a data structure used for storing, retrieving and sorting data in an efficient way by using a binary tree structure with the property that the keys in a node’s left subtree are less and the keys in a node's right subtree are greater than the key of the node itself, and then making it balanced.

    The BSTLearner tutorial explains the usage and implementation of an interactive binary search visualization in Python using Graphviz and Jupyter Notebook Widgets. Jupyter Notebook Version 1.0 BSTLearner 1.0.ipynb contains intermediate steps and test code. In Jupyter Notebook Version 1.2 BSTLearner 1.2.ipynb the classes have been moved to a Python module algolibs, so the notebook now is uncluttered and contains only the GUI / widgets code. This is work in progress! To be finished as project work by students.

  • DecisionTreeLearner   DecisionTree Failure Classification Python

    The interactive DecisionTreeLearner shows the training and usage of a decision tree model for failure prediction on a small automotive data set.

    We first implemented the basic functionality of classification with Scikit-learn decision trees. Next, we created an interactive visualization of the decision tree using the Jupyter widget function interactive_output() with the aim of testing the effect of the different parameters on the performance of the model. The user interface has three tabs ("Data", "Model", and "Prediction"), each tabs has a top panel which is used to set the most important configuration parameters, and a bottom panel that shows the tabular or graphical output. Whenever a configuration parameter is changed, a new decision tree model is immediately created and visualized. Simultanously, the performance indicators for the validation data set are automatically calculated and displayed.

  • RVisLearner   Data Visualization ggplot2 R

    The interactive RVisLearner shows how to create data visualizations with the base R functions hist and plot from the graphics package, and with the corresponding functions of the Tidyverse ggplot2 package.

  • Courses

    The courses listed below are open to students enrolled in the engineering programms of the University of Applied Sciences Kaiserslautern.

  • Algorithms »
      Sorting Searching   Trees   Graphs   Java   Python

    The course Algorithms gives an introduction to algorithms and data structures as well as basic principles of algorithm design and complexity analysis. Assignments consist in extending the implementation of an existing algorithms framework in Java or Python.

    Bachelor's level, Electrical Engineering, Winter term
  • Database Systems »
    DBMS Relational Model   ER-Diagrams   SQL   XML   JSON

    The course Database Systems teaches basic concepts of databases and database management systems (DBMS), with focus on relational databases and an overview on NoSQL databases.

    Master's level, Electrical Engineering, Winter term
  • Web Technologies and Cloud Computing »
    HTML, CSS, JavaScript, PHP Web Frameworks   Web Services   Cloud

    The course Web Technologies and Cloud Computing gives an introduction to the theoretical and practical basics of modern web and cloud technologies: Internet, WWW, server and client-side programming, web frameworks: Java-based, PHP-based, Web services and service-oriented architectures, basics of cloud computing, service models and operator models.

    Master's level, Electrical Engineering, Winter term
  • Numerical Methods »
    Modeling Integration   ODEs   PDEs   MATLAB

    The course Numerical Methods enables students to build mathematical models for common classes of engineering problems and solve the problems by implementing appropriately chosen numerical methods in MATLAB. The focus is on solving ordinary differential equations (Euler, Runge-Kutta) and partial differential equations (finite difference and finite element methods).

    Master's level, Mechanical Engineering, Summer term
  • Projects

    elab2go - Mobile Engineering Lab is an online platform for demonstrators showcasing new technologies and trends (in German). Current demonstrators and tutorials explain basics concepts and methods of Applied Machine Learning, Predictive Maintenance, and Internet of Things. The platform is developed as part of the "Offene Digitalisierungsallianz Pfalz"-Project, and aims to transfer innovation and trend technologies from universities in the Rhineland-Palatinate region.

    The series of demonstrators and tutorials Demo-PYx introduces to models and algorithms of Machine Learning, e.g. Decision Trees, Cluster Analysis, Artificial Neural Networks) by using Python as programming language, the Python-packages for data analysis Pandas, Scikit-Learn, Keras, Tensorflow, and Jupyter Notebook or Google Colab as web-based IDE.

    The survey article Machine Learning: Concepts, Methods, Tools gives an introduction to the most important concepts of machine learning and explains the use of the elab2go demonstrators that illustrate them.

    The survey article An introduction to Predictive Maintenance explains the fundamental concepts and basic technologies of predictive maintenance, as well as their areas of application in practice, solutions of global players and an overview of the elab2go demonstrators that illustrate data analysis as part of Predictive Maintenance process.

    University of Applied Sciences Kaiserslautern

    The Department of Applied Engineering (AING) offers students the opportunity to pursue an exceptional, high quality education in the largest electrical and mechanical engineering department in Rhineland-Palatinate.

    New image trailer: "AING 2021"

    Our department has a shiny new image trailer (in German). Starting at 00:49, you can see some of the ongoing projects and labs of the department, such as roboters, drones and high voltage lab. The persons in the trailer are not really our professors and students, since currently it is difficult to film on site. You can look up the original faces on our website.



    Events

    Open Campus HS KL

    On 21.05.2022 the University of Applied Sciences Kaiserslautern invited to the "Open Campus 2022". The event offered prospective students all the information on studying, teaching and research at the university, as well as the opportunity to see the facilities and study conditions up close and personal. The interested public could attend presentations of the university's programs, in particular Mechanical and Electrical Engineering. Here you can download the presentation of the Electrical Engineering program.

    Talk series STUDIUM & BERUF

    On 15.02.2022 at 1:30 pm, high school students attended online via BigBlueButton the web-event STUDIUM & BERUF, where they are informed about the courses on offer at the University of Applied Sciences Kaiserslautern. Here you can download the presentation that was held this year, (in German) with details about our Electrical Engineering program. The full program and registration modalities can be found on the website of the university following this link: hs-kl.de/vortragsreihe.