Principles of Supervised Machine Learning, including algorithms, meta-algorithms, evaluation, ...
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, Support Vector Machines, Random Forests, Hidden Markov Models, 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 algorithms
Assessment: is based on written exam, report, and implemented machine learning algorithm
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
Two chapters of Data Mining/Business Intelligence (188.429) courses will be posted in TUWEL. If you did not attend this course before, please read this material before the first class of Machine Learning course.
Self-Organising Systems (188.413) offers complementary topics in unsupervised data analysis. Information Retrieval (188.412) applies principles from Data Mining, Machine Learning and Self-Organising Systems for text and music retrieval and information extraction.
