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Introduction to Probabilistic Reasoning and Numerical Methods in Machine Learning

Module PH2304

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.

Basic Information

PH2304 is a semester module in English language at Master’s level which is offered irregular.

If not stated otherwise for export to a non-physics program the student workload is given in the following table.

Total workloadContact hoursCredits (ECTS)
150 h 150 h 5 CP

Responsible coordinator of the module PH2304 is Allen C. Caldwell.

Content, Learning Outcome and Preconditions


This course will introduce the basic concepts of reasoning under uncertainty, as well as provide an overview of common methods for data processing, optimization, and machine learning. After an introduction to probability theory and common probability distributions, we discuss inference tasks with various probabilistic models and continue by outlining methods to approach more involved inference tasks through approximation or sampling. From there we continue by presenting the basics of data de-correlation, reduction, and optimization algorithms. We continue with classical machine learning topics, such as clustering, classification, and regression with tree-based- and neural-network-algorithms. In the end, several deep learning models and architectures will be introduced and discussed.

Learning Outcome

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Linear Algebra, basic Analysis, a programming language of choice recommended.

Courses, Learning and Teaching Methods and Literature

Courses and Schedule

VI 1 Introduction to Machine Learning Caldwell, A.
Assistants: Eller, P.
Tue, 10:00–14:00, virtuell
VI 1 Introduction to Probabilistic Reasoning Caldwell, A.
Assistants: Knollmüller, J.
Tue, 10:00–14:00, virtuell

Learning and Teaching Methods

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Module Exam

Description of exams and course work

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Exam Repetition

The exam may be repeated at the end of the semester. There is a possibility to take the exam in the following semester.

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