This course briefly covers basic statistics and probability concepts and introduces techniques to model and analyze probabilistic data. This includes basic representation such as Bayesian networks as well as hypothesis testing techniques for data analysis and interpretation. Further, it introduces modeling and analysis techniques for sequential processes, including Markov models, regression analysis, and basic queueing models. All of these techniques will be discussed in the context of common Computer Science problems from a wide range of fields, including Computer Networks, Artificial Intelligence, Machine Learning, Computer Vision, Data Mining, Bioinformatics, etc. In addition, the course will discus selected advanced topics and applications such as capacity planning and bottleneck analysis, clustering and classification
Students successfully completing this course will have gained a solid understanding of probabilistic data modeling, interpretation, and analysis an thus have formed an important basis for more advanced courses in Computer Science as well as for the handling and analysis of data used in real-life applications and research.
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