Information on individual educational components (ECTS-Course descriptions) per semester | |
Degree programme: | Master Computer Science |
Type of degree: | FH Master“s Degree Programme |
Full-time | |
Summer Semester 2022 | |
Course unit title | Machine Learning |
Course unit code | 024912020701 |
Language of instruction | English |
Type of course unit (compulsory, optional) | Compulsory |
Semester when the course unit is delivered | Summer Semester 2022 |
Teaching hours per week | 2 |
Year of study | 2022 |
Level of course unit (e.g. first, second or third cycle) | Second Cycle (Master) |
Number of ECTS credits allocated | 4 |
Name of lecturer(s) | Sebastian HEGENBART |
Prerequisites and co-requisites |
Fundamental concepts of probability theory such as distribution, expectation value, standard deviation, conditional probabilities. Introduction to the tool WEKA (LV 024912010701 Data Organization) |
Course content |
General framework for machine learning, in particular a framework for modelling uncertainty as the starting point for all learning methods used in this course Workflow model for the planning and execution of an ML project Introduction to text and activity classification Algorithms such as Multinomial Bayes, Random Forest, Support Vector Machines, in basic understanding, not in full detail Processing and pre-processing of large data sets, using the two example domains Introduction of a second tool for machine learning (e.g. SPSS). |
Learning outcomes |
Students are able to describe how computers can be used to solve tasks for which they have not been explicitly programmed, especially in the field of classification, pattern recognition, meaningful data aggregation (clustering), speech and image recognition, less / only on the edge in the area of intelligent control. address the problem of several, different solutions and how to evaluate them and select one. explain the setup and operationalization of an application in the area of machine learning by means of an example. They can use their first practical experience with text classification / search engines or Human Activity Recognition (HAR). design the process and use the tools to set up and operationalize an application w ithin two different tool environments. |
Planned learning activities and teaching methods |
Lecture + in-class exercises, homework every two weeks. |
Assessment methods and criteria |
Continual assessment. Evaluation of exercises. |
Comment |
None |
Recommended or required reading |
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Mode of delivery (face-to-face, distance learning) |
Face-to-face |
Summer Semester 2022 | go Top |