| Prerequisites and co-requisites |
Lecture series on discrete mathematics and linear algebra and calculus |
| Course content |
- Classical probability theory (random experiments, classical probability, reliability theory, conditional probability, Bayes formula),
- random variables (discrete and continuous random variates, cumulative distribution function and probability density function, moments, variance, median, quantile, covariance),
- discrete distributions (Bernoulli, binomial, geometric, Poisson distribution),
- continuous distributions (uniform, exponential, normal distribution),
- central limit theorem,
- descriptive statistics (graphics, numerical descriptors)
- inferential statistics (point estimates, confidence interval, hypothesis testing, linear regression)
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| Learning outcomes |
Technical and methodological competence (F / M)
- The students master elementary basics of applied probability calculation and statistics, with an application focus on computer science.
- The students have the skills to understand specialist texts that use probability theory terminology and to work on simple problems / applications in the field of probability calculation.
- The students know the most important discrete and continuous random distributions.
- The students master the methods of descriptive statistics and can use essential methods of inductive statistics, such as point and interval estimation methods and hypothesis tests.
Through specifically selected forms of learning and teaching, this course also contributes to the training of the following general skills:
Social and communicative competence (S / K)
- Reliability: Comply with rules and agreements and do your own work in the promised quality
Self-competence (S)
- Self-reflection ability: Knowing your own abilities and limits and reflecting on your own actions
- Learning competence and motivation: Ability and willingness to acquire new knowledge independently and to learn from successes and failures
- Adaptability: Engaging in changing conditions and being able to deal with changing situations
- Expressiveness: Ability to use a clear and understandable form of expression and written language as well as a situation-appropriate choice of words
Transfer Competence (T)
- Ability to analyze and present / communicate: Capture and organize assets, extensive and complex relationships in a short time, filter out the essentials and present them in a generally understandable way
- Assessment and problem-solving ability: assessing facts and being able to use them to derive consequences and approaches
- Organizational skills: Be able to implement goals in work tasks and make optimal use of the available resources
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| Planned learning activities and teaching methods |
Lectures, group exercises with homework presentations. Individual feedback on group and home exercises. |
| Assessment methods and criteria |
- Exercises: 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. 3rd Ed. Boca Raton: Chapman and Hall/CRC.
Teschl, Gerald; Teschl, Susanne (2013): Mathematik für Informatiker: Band 1: Diskrete Mathematik und Lineare Algebra. 4th rev. Ed. 2013. Berlin Heidelberg: Springer Spektrum.
Teschl, Gerald; Teschl, Susanne (2014): Mathematik für Informatiker: Band 2: Analysis und Statistik. 3rd rev. Ed. 2014. Berlin Heidelberg: Springer Vieweg.
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| Mode of delivery (face-to-face, distance learning) |
Classroom teaching with mandatory attendance in the seminars. |