Inhalt

[ 993SEPTPRWP19 ] PR Practical Work in AI (Master)

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
7,5 ECTS M2 - Master's programme 2. year (*)Artificial Intelligence Sepp Hochreiter 5 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Artificial Intelligence 2025W
Learning Outcomes
Competences
Students have advanced practical research skills in AI, including the ability to independently analyze, improve, and compare algorithms or solve theoretical AI problems. They have experience in handling real-world AI challenges, from algorithm implementation to data analysis, and learn how to effectively communicate their results through code, written reports, and presentations.
Skills Knowledge
  • Analyzing and Understanding AI Algorithms (k5)

Students are able to analyze and understand published AI algorithms, identifying their strengths, limitations, and areas for improvement.

  • Implementing and Improving AI Models (k5)

Students can implement AI algorithms computationally, improve them by applying new techniques or optimizations, and test their performance on relevant datasets.

  • Comparing and Evaluating AI Techniques (k5)

Students are capable of comparing different AI models or algorithms, using metrics and statistical methods to evaluate their performance in various scenarios.

  • Solving Theoretical Problems in AI (k5)

Students are able to solve more theoretical AI problems mathematically, using logical reasoning and mathematical tools to explore AI concepts and models.

  • Presenting Results and Writing Reports (k6)

Students can effectively communicate their findings, writing a comprehensive report that details their approach, results, and conclusions, as well as presenting their work to an audience.

Students acquire knowledge of advanced AI algorithms and methods, gaining a deeper understanding of their application, strengths, and limitations. They learn to solve real-world AI problems and present their research through written reports and presentations, while gaining hands-on experience in improving and evaluating existing algorithms.
Criteria for evaluation Combined assessment of the implemented code, the project presentation, and the final report
Methods Literature, data, and software are provided to students on an individual basis
Language English
Changing subject? No
On-site course
Maximum number of participants 15
Assignment procedure Direct assignment