Math for Computer Scientists 3 (MA3)
3. Semester
5 ECTS | 4 SWS
Written Exam 90 Minutes (K90)
Analyze data systematically: You will learn combinatorics, probability theory, and statistics to solve data-driven problems in computer science in a well-founded manner.
Contents
- Combinatorics
- Classical Probability and Random Experiments
- Sample Space, Elementary Events, Events, Axioms of Probability Theory, Rules of Probability, Relationship to the Classical Definition
- Conditional Probability, Law of Total Probability, Bayes` Theorem
- Random Variables, Probability Mass and Density Functions, Distribution Functions, Expected Value, Variance, Standard Deviation, Computational Rules
- Central Limit Theorem, De Moivre–Laplace Theorem
- Covariance and Correlation, Computational Rules
- Fundamentals of Statistics
- Parameter Estimation
- Regression Analysis
- Maximum Likelihood Estimation (MLE)
- Confidence Intervals
- Hypothesis Testing: Student`s t-Distribution, Chi-Square Distribution, ROC Curves
Competencies
Students are able to
- apply statistical methods to given problems.
Literature
- Lothar Papula: Mathematik für Ingeneure und Naturwissenschaftler, Band 3, Viewegs Fachbücher für Technik, 1999
- P.Hartmann: Mathematik für Informatiker: Ein praxisbezogenes Lehrbuch, Vieweg Teubner, 2012
Lecturer
- Prof. Dr. Elena Fimmel
- Prof. Dr. Miriam Föller-Nord
- Prof. Dr. Lutz Strüngmann
- Dr. Yordan Todorov
- Prof. Dr. Ivo Wolf
Recommended Previous Knowledge
Module Details
| Semester |
3 |
| Lecture Language |
German |
|
Frequency
|
Every Term
|
| Credit Points (ECTS)
|
5 |
| Course Coordinator |
Prof. Dr. Elena Fimmel |
| Duration |
1 Semester |
|
Course Achievement
|
None |
|
Prerequisite for exam
|
Compulsory Assignment (PU) |
|
Exam
|
Written Exam 90 Minutes (K90) |
Weekly Hours (SWS)
Work Load
| Lecture |
60 h |
| Self Study |
90 h |
| Sum |
150 h |
