Machine Learning

Course content

Machine learning is a key to analyze data in different science and engineering disciplines. This course will provide an introduction to the fundamental methods at the core of machine learning, including - but not limited to - classification, regression analysis, clustering, and dimensionality reduction. This course is designed for Bachelor students in different disciplines who employ machine learning algorithms in their fields. Students will learn about the basic concepts of machine learning and will apply the learned concepts on the practical problems using open source libraries from the Python programming ecosystem. The course will also briefly cover neural networks and will be closed by a short introduction to deep learning. Classes on theoretical aspects will be complemented by practical lab sessions. In this course we do not concentrate on a specific type of data and various datasets will be used in the practical example. Upon completion of the course, the students will be able to understand basic principles of ML techniques and to apply them for simple problems.

Content:

• Linear regression
• Cost function and optimization
• Gradient descent
• Performance assessment
• Logistic regression
• Nearest neighbor and KNN Decision trees
• SVM
• Non-supervised learning, clustering Dimensionality reduction and PCA Ensemble and boosting methods
• Neural Networks I Neural Networks II
• Introduction to Deep learning