After successful completion of the course, students are able to...
- Formulate problems as specific Machine Learning tasks
- Understand of a range of machine learning algorithms and their characteristics
- Select the fitting methods for a specific learning goal
- Explain data preprocessing techniques
- Evaluate the methods for their suitability
Principles of Supervised and Unsupervised Machine Learning, including pre-processing and Data Preparation, as well as Evaluation of Learning Systems. Machine Learning models discussed may include e.g. Decision Tree Learning, Model Selection, Bayesian Networks, Regression techniques, Support Vector Machines, Random Forests as well as ensemble methods.Didactical Concept:-Lectures-Exercises:students will compare different machine algorithms for particular data sets, and have to implement a machine learning algorithm - Presentation of algorithms by students - Discussion of reports that summarize the comparison of machine learning algorithmsAssessment: is based on written exam, report, and implemented machine learning algorithms
Preliminary talk (Vorbesprechung) & intro: 3.3. 2020
The course contains classroom lectures and exercises. Exercises include the application of machine learning techniques for various data sets and implementation of machine learning algorithms. The exercises are prepared at home and will be presented/discussed during the exercise classes.
This course will be held in both summer and winter term from, summer semester 2019 on.
This course will be held completely in TUWEL - all lecture materials and news about the lecture will be made available there, and all questions regarding the course should be asked in the TUWEL forum *only*, not via TISS.To get access to the TUWEL course, just apply to the group in TISS, and then follow the TUWEL link above
ECTS Breakdown:
13 classes (including prepration): 34 h
3 classes for presentations/discussions (including preparation): 9
Assignments: 39.5 h
exam: 30 h
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total: 112.5 h
- Solving of exercises regarding experiments in machine learning, using a software toolkit of the student's choice (e.g. Python scikit-learn, Matlab, R, WEKA, ...): 50%
- Written exam at the end of the semester: 50%
Self-Organising Systems (188.413) offers complementary topics in unsupervised data analysis. Information Retrieval (188.412) applies principles from Data Mining, Machine Learning
As a subsequent course, Problem Solving and Search in Artificial Intelligence (181.190) teaches some problem solving techniques that can be used in machine learning.
