Introduction to Data Analysis Techniques

The module consists of two parts:

The first part is focusing on methods for data processing, optimization and machine learning. First we will learn the basics of data decorrelation, reduction and optimization algorithms. Based on these new skills, we dive into machine learning topics, such as clustering, classification and regression with tree based algorithms and neural networks. In the last part deep learning models and different architectures will be introduced and explained.

In the second part we will build on top of the prior week and discuss deep learning in depth starting from a short review of the nature of machine learning followed by a broad overview over popular deep learning architectures combined with hands-on experience in training basic deep networks.

After successful completion of the module the students will have earned a introductory competence to carry out statistical analysis through both of the major schools of statistics: Frequenist and Bayesian analysis with hands on experience on concrete examples

For part 1:  Linear Algebra, basic Analysis, a programming language of choice

For part 2: basic probability theory, statistics, a programming language of choice