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| Detailinformationen |
| Quellcurriculum |
Bachelorstudium Artificial Intelligence 2019W |
| Ziele |
(*)The students learn the basics in statistics like descriptive statistics, principles of probabilities, random variables, distributions including test distributions, parameter estimation including point and interval estimations, and testing hypotheses. The statistic skills are extended to non-linear optimization, the EM algorithm, pseudorandom number generation, MCMC methods, Jackknife and bootstrap, and numerical integration. Furthermore, basic methods of data analysis like PCA, clustering, and linear models are covered in this subject. Other topics are data visualization including scatter plots, box plots, and densities using PCA or factor analysis as well as visual analytics comprising information visualization and scientific visualization that focuses on analytical reasoning facilitated by interactive visual interfaces. Students will also learn about user-generated data including sources and methods for data extraction from the web and social media and techniques for usage of this data as well as about computational data analytics. Another central subject matter is Natural Language Processing using modern machine learning and deep learning methods which include text processing, speech processing, translation, sentiment analysis, text classification, abstract generation and similar. In addition, students acquire knowledge in digital signal processing including audio and speech processing, sonar, radar and other sensor array processing, spectral density estimation, statistical signal processing, and digital image processing.
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| Lehrinhalte |
(*)The contents of this subject result from the contents of its courses.
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