| Prerequisites and co-requisites |
Courses in the Mathematics Module: “Discrete Mathematics” and “Linear Algebra and Analysis” |
| Course content |
- Elementary Probability Theory (random experiments, probability, urn scheme, reliability problems, conditional probability, Bayes’ theorem)
- Random Variables (discrete and continuous random variables, distribution and density function, moments, variance, median, quantile, covariance)
- Discrete Distributions (Bernoulli, geometric, binomial, Poisson distribution)
- Continuous Distributions (uniform, exponential, normal distribution)
- Multidimensional Distributions
- Central Limit
- Theorem Descriptive Statistics (graphical representation, sample statistics)
- Inferential Statistics (point estimation, confidence intervals, hypothesis tests, linear regression)
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| Learning outcomes |
Subject and Methodological Competence (F/M)
- Students master the elementary basics of applied probability theory and statistics, with a focus on applications in computer science.
- They have the skill to understand technical texts that use probabilistic terminology and to work on simple problems/applications in the field of probability theory.
- Students know the most important discrete and continuous probability distributions.
- They master the methods of descriptive statistics and can apply essential methods of inferential statistics, such as point and interval estimation procedures and hypothesis tests.
Through specifically selected learning and teaching methods, this course also contributes to the development of the following interdisciplinary competencies:
Social and Communication Competence (S/K)
- Reliability: Adhering to rules and agreements and completing one’s tasks with the promised quality
Self-Competence (S)
- Self-reflection ability: Knowing one’s own abilities and limits and reflecting on one’s actions
- Learning competence and motivation: Ability and willingness to independently acquire new knowledge and learn from successes and failures
- Adaptability: Being able to adapt to changing conditions and handle varying situations
- Expressiveness: Ability to use clear and understandable spoken and written language and to choose words appropriately for the situation
Transfer Competence (T)
- Analytical and presentation/communication skills: Ability to quickly grasp and organize extensive and complex contexts, filter out the essentials, and present them in an understandable manner
- Judgment and problem-solving ability: Ability to assess situations and derive consequences and solutions
- Organizational ability: Ability to translate goals into work tasks and optimally utilize available resources
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| Planned learning activities and teaching methods |
Lecture and group exercises with presentation of homework assignments. Individual feedback on group and homework assignments. |
| Assessment methods and criteria |
Exercises with project: 40%
Written final exam: 60% (needed to be positive)
For a positive grade, overall across all parts of the examination a minimum of 50% of the possible points must be achieved AND in the written final exam a minimum of 50% of the points must be achieved. |
| Comment |
Non applicable |
| Recommended or required reading |
- Baron, Michael (2019): Probability and Statistics for Computer Scientists. 3. Aufl. Boca Raton: Chapman and Hall/CRC.
- Teschl, Gerald; Teschl, Susanne (2013): Mathematik für Informatiker: Band 1: Diskrete Mathematik und Lineare Algebra. 4., überarb. Aufl. 2013. Berlin Heidelberg: Springer Spektrum.
- Teschl, Gerald; Teschl, Susanne (2014): Mathematik für Informatiker: Band 2: Analysis und Statistik. 3., überarb. Aufl. 2014. Berlin Heidelberg: Springer Vieweg.
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| Mode of delivery (face-to-face, distance learning) |
On-site course with mandatory attendance in the exersises. |