Hasso-Plattner-InstitutSDG am HPI
Hasso-Plattner-InstitutDSG am HPI

Probabilistic Models: Modeling, Learning and Analysis (Wintersemester 2022/2023)

Dozent: Prof. Dr. Holger Giese (Systemanalyse und Modellierung) , Christian Medeiros Adriano (Systemanalyse und Modellierung) , Dr. Maria Maximova (Systemanalyse und Modellierung) , Dr. Sven Schneider (Systemanalyse und Modellierung)

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 01.10.2022 - 31.10.2022
  • Lehrform: Vorlesung
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Englisch

Studiengänge, Modulgruppen & Module

IT-Systems Engineering MA
  • IT-Systems Engineering
    • HPI-ITSE-A Analyse
  • IT-Systems Engineering
    • HPI-ITSE-E Entwurf
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-K Konzepte und Methoden
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-T Techniken und Werkzeuge
Data Engineering MA
Digital Health MA
Software Systems Engineering MA


Models play a major role in the development of software and systems.  They are, for example, often employed to capture the needs of the users concerning the data (database schemas), behavior (system processes), or both for an envisioned or existing system, or they are used to capturing how the system is or should be structured (e.g., architecture, classes and associations) or behave. However, the classical models employed in software engineering do not support the uncertainty of an envisioned or existing systems that can also have a probabilistic nature -- meaning that certain aspects follow given probabilities or probabilistic distributions. 

On the one hand this enables modeling how the system is or should behave including probabilistic aspects. On the other hand, probabilistic models often enable  learning from observations,  which is often not feasible to the same extent for the classical counterparts. Finally, in many cases the probabilistic nature still permits an analysis of the likely behavior of the systems as predicted by the models.

Therefore, we will study how to represent various probabilistic aspects by building (learning) and analyzing (checking) different probabilistic models. We will cover models like discrete and continuous Markov-Chains, Hidden Markov Models, Markov Decision Processes, Semi-Markov Processes, etc.

Goal:  Provide students with the foundations of various modern machine learning methods, for self-surpervised learning, reinforcement learning, and representation learning, as well as for the verification of safety-critical systems like traffic management and self-driving platforms.


Basic knowledge of software modeling, as taught in the Modeling I courses or modeling languages and corresponding formalisms.


The slides for the lecture as well as a list of literature are provided in the internal area.

Lern- und Lehrformen

The lecture is accompanied by a project that includes introductory exercises.


Performance is recorded by means of a project to be worked on during the semester and an oral examination at the end of the semester. The final grade results from the weighted average of both parts. The project grade is weighted at 1/3, the exam grade at 2/3. Working on the project is a prerequisite for admission to the oral examination. The project includes an introductory phase, in which some exercises are worked on first.


The first lecture will take place on October 18, 2022 (Tuesday) from 09:15-10:45. The lectures will take place in room L-1.02 and remotely via Zoom (credentials)*

We will follow the recurrent schedule of:

  • Tuesdays from 09:15-10:45 in room L-1.02
  • Wednesdays from 09:15-10:45 in room L-1.02

* In case that you do not have access to GitLab, please email christian.adriano [at] hpi.de