Introduction to Data Analysis Techniques

Module PH2309

This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.

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

Basic Information

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

Content

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.

Learning Outcome

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

Preconditions

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

SS 2022 WS 2021/2 WS 2020/1

Type	SWS	Title	Lecturer(s)	Dates	Links
VO	2	Introduction to Machine Learning	Heinrich, L. Assistants: Eller, P.

Learning and Teaching Methods

The course is given in a intensive "Block Course" format with 3 days of lectures introducing the material and subsequent tutorials to gain hands-on experience with the methods.

Media

The lectures are given in an online format and will be available as recordings for later access. Lecture notes will be provided in the form of slides or scanned notes.

Literature

- G. Cowan, Statistical Data Analysis, Clarendon Press, Oxford, 1998; - R.J.Barlow, A Guide to the Use of Statistical Methods in the Physical Sciences, John Wiley, 1989; - L.Lyons, Statistics for Nuclear and Particle Physics, CUP, 1986.

Module Exam

Description of exams and course work

Exercises will be given for both Bayesian and Frequentist analysis examples. To receive full credit students will need to turn in solutions to all exercises. for both parts of the Block Course (tutoring + homework exercises). The grade for the course will be based on the two sets of exercises, and there will not be an additional exam.

Exam Repetition

The exam may be repeated at the end of the semester.