Machine Learning and Data Analytics
Teaching in summer term
The course Machine Learning and Data Analytics (Lecture + Exercise) is delivered as an online course. Lectures and exercises follow the Inverted Classroom paradigm. Recordings of lectures and exercises will be made available and are accompanied by lecture and exercise live classes. Please note: Recordings and other material will be made available over time and then remain available until the end of the semester. Live sessions (lectures and exercises) are not recorded. During the live sessions, specific questions regarding the subjects treated in the recordings will be answered and there will be time for discussions. The subjects taught in the recorded sessions will not be repeated. Detailed information about lectures and exercises live sessions will be made available via Moodle.
The science of learning plays a key role in the fields of statistics, data mining, and artificial intelligence, intersecting with areas of engineering and other disciplines.
This introductory course on machine learning will provide an overview of many concepts, techniques and algorithms for learning from data. These methods can be classified into supervised and unsupervised learning. In supervised learning, the goal is to predict the value of an outcome measure based on a number of input measures. Some of the methodologies belonging to this category are: linear regression and logistic regression, boosting, and support vector machines. In unsupervised learning, there is no outcome measure, and the goal is to describe the associations and patterns among a set of input measures. Among the methodologies of unsupervised learning, there are: clustering, outlier detection, dimensionality reduction, and association methods. Ways of assessing the effectiveness of these methods will be presented, together with practical problems which will be solved through the use of Python3 Jupyter Notebook.
No formal requirements. Suggested requirements: knowledge of basic concepts of linear algebra, probability, and statistics and some familiarity with Python are recommended.
|Grading:||60 % exam + 40 % group project + bonus possible: Quizzes will be asked during classes. By completing at least 75% of them successfully, the final grade will be increased by one grade step, e.g., from 2.0 to 1.7.|