They were programming a neural network that composes music? This is what students of electrical engineering and information technology do. The budding engineers have to get one of the 30 places in the laboratory for applied machine learning algorithms (LAMA). To be on the list, they must have basic programming skills and knowledge of information technology, signal and system theory, and probability theory.
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Learn AI With Supercomputers
If you have made it, a high-performance computer of the latest generation is available in the laboratory. According to the KIT, students should independently implement algorithms and structures such as a perceptron, decision trees, or an evolutionary algorithm and learn the methods and tools commonly used in business and science today.
Engineering Students Develop Their Own AI Applications
Once the basics have been laid, the four-week practical phase follows, also called the “Into the wild” step by KIT. During this phase, the engineering students have four weeks to implement their projects. In addition to the composing network, the students also developed a solar power forecast model and a back-friendly office chair.
Machine Learning As A Master’s Degree
” There are no admission restrictions. Engineers can be admitted if they have the necessary mathematics and computer science skills. “During the lectures, exercises are carried out in which the students implement smaller systems, such as image recognition.” In addition, the students complete an internship. “This internship goes through the entire development cycle of a machine learning problem. Starting with the concrete naming of the problem, through the suitable selection of the methods up to the evaluation of the learning process “, says the professor of machine learning. Current research questions, e.g., bioinformatics, medicine, neurosciences, or physics, are to be dealt with. High-performance computers are also used here. “Such internships are still rare because they are very support-intensive, and currently, only a few locations are equipped with sufficient high-performance computers and appropriately qualified staff.” However, he is certain that such internships will be offered more frequently in the future, as machine learning is becoming more and more important.
“The problem is rather that there are currently too few professorships in this area.” Neuroscience or physics. High-performance computers are also used here.” he is certain that such internships will be offered more frequently in the future, as the topic of machine learning is becoming more and more important. Because they are very support-intensive, currently, only a few locations are equipped with sufficient high-performance computers and appropriately qualified staff”. However, he is certain that such internships will be offered more frequently in the future, as machine learning is becoming more and more important. “The problem is rather that there are currently too few professorships in this area .” because they are very support-intensive. Currently, only a few locations are equipped with sufficient high-performance computers and appropriately qualified staff”. However, he is certain that such internships will be offered more frequently in the future, as machine learning is becoming more and more important.
What Is The Next Step With Machine Learning?
Kristian Kersting and Volker Tresp see it that way too. They are members of the platform learning systems launched by the Federal Ministry of Education and Research (BMBF). Your goal is to advance the topic of “Artificial Intelligence. ”
In their whitepaper “Machine and deep learning: The engine for AI made,” the two university professors explain that further investments in cutting-edge AI research are necessary to ensure AI research’s competitiveness in Germany and keep up with international research. They were keeping pace with developments. In addition, they demand that interdisciplinary networking with other sciences, such as statistics, electrical engineering, or mechanical engineering, be promoted, just like the creation and promotion of ecosystems in which AI knowledge can be informally shared and further developed. “Companies, universities, research institutions, and start-ups must be brought together geographically so that the talents can easily work together and exchange ideas,” such a conclusion of the researchers.
