Introduction to Data Analysis Techniques
Module version of SS 2022 (current)
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|SS 2022||WS 2020/1|
PH2309 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Specific catalogue of special courses for nuclear, particle, and astrophysics
- Complementary catalogue of special courses for condensed matter physics
- Complementary catalogue of special courses for Biophysics
- Complementary catalogue of special courses for Applied and Engineering Physics
If not stated otherwise for export to a non-physics program the student workload is given in the following table.
|Total workload||Contact hours||Credits (ECTS)|
|150 h||60 h||5 CP|
Responsible coordinator of the module PH2309 is Lukas Heinrich.
Content, Learning Outcome and Preconditions
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
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Introduction to Machine Learning||
Assistants: Eller, P.
Learning and Teaching Methods
Description of exams and course work
The exam may be repeated at the end of the semester.