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Introduction to Machine Learning

Module PH8127

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

PH8127 is a semester module in English language at which is offered irregular.

This Module is included in the following catalogues within the study programs in physics.

  • Subject-Related Qualification Modules for Doctoral Candidates in Physics (lecture series)

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

Total workloadContact hoursCredits (ECTS)
 h 15 h  CP

Responsible coordinator of the module PH8127 is Allen C. Caldwell.

Content, Learning Outcome and Preconditions


This course 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.

Learning Outcome

After successful completion of the module the students are able to:

  1. basic data transformations
  2. knowledge in various optimization algorithms
  3. k-means clustering
  4. decision trees,
  5. networks
  6. convolutional neural networks
  7. auto-encoders
  8. generative models


Linear Algebra, basic Analysis, a programming language of choice

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

Learning and Teaching Methods

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Module Exam

Description of exams and course work

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