Prof. Dr. Claus Möbus

Room: A02 2-226

Tel: +49 441 / 798-2900 



Manuela Wüstefeld

Room: A02 2-228

Tel: +49 441 / 798-4520 



Course Offering

We offer two seminars "Probabilistic Modeling I (Inf 533) / II (534)"

Seminar Probabilistic Modeling I (Inf 533) / II (Inf 534)

The seminar consists of two parts, which take place in the winter semester (Part I) and in the summer semester (Part II).
In Part I we will deal with the state-of-the-art (SoA) in probabilistic modeling using probabilistic programming languages (as an example the DSL WebPPL) in cognition (perception, problem solving, evaluation, decision making) and behavioral control of predominantly natural agents.
Part II deals with probabilistic modeling in technical, epidemiological and ecological contexts.We will also consider the SoA in the development of new probabilistic modeling languages (e.g. PyRo, TURING, Greta, PyMC3).
This division of the contents adapts to the students' previous knowledge and wishes.
The successful participation in both seminars can be combined in such a way that it corresponds to a lecture module.



Transpilation of SICP into Julia/Pluto.jl

Learning Julia/Pluto.jl by Climbing the SICP-Mountain

"Structure and Interpretation of Computer Programs (SICP) is a computer science textbook by Massachusetts Institute of Technology professors Harold Abelson and Gerald Jay Sussman with Julie Sussman. It is known as the Wizard Book in hacker culture.[1][2] It teaches fundamental principles of computer programming, including recursion, abstraction, modularity, and programming language  design and implementation." (Wikipedia, 2021/09/15)

SICP-scripts are written in MIT-SCHEME invented in the 90ies at the MIT AI Lab. Scheme is a minimalist dialect of the Lisp family of programming languages. JULIA was also but nearly 30 years later invented at the very same MIT. This happened 2009 not at the MIT AI Lab but at MIT's Computer Science and AI Laboratory (CSAIL). Here we present transpilations into JULIA within a Pluto.jl-embedding.

  1. Building Abstractions with Procedures
    1. The Elements of Programming
      1. Expressions
      2. Naming and the Environment
      3. Evaluating Combinations
      4. Compound Procedures
      5. The Substitution Model for Procedure Application
      6. Conditional Expressions and Predicates
      7. Example: Square Roots by Newton's Method
      8. Procedures as Black-Box Abstractions
    2. Procedures and the Processes They Generate
      1. Linear Recursion and Iteration
      2. Tree Recursion
      3. Orders of Growth
      4. Exponentiation
      5. Greatest Common Divisors
      6. Example: Testing for Primality
    3. Formulating Abstractions with Higher-Order Procedures
      1. Procedures as Arguments
      2. Constructing Procedures Using Lambda
      3. Procedures as General Methods
      4. Procedures as Returned Values
  2. Building Abstractions with Data
    1. Introduction to Data Abstractions
      1. Example: Arithmetic Operations for Rational Numbers
      2. Abstraction Barriers
      3. What is Meant by Data ?
      4. Extended Exercise: Interval Arithmetic
    2. Hierarchical Data and the Closure Property
      1. Representing Sequences
      2. Hierarchical Structures
      3. Sequences as Conventional Interfaces
      4. Example: A Picture Language
    3. Symbolic Data
      1. Quotation
      2. Example: Symbolic Differentiation
      3. Example: Representing Sets
      4. Example: Huffman Encoding Trees
    4. Multiple Representations for Abstract Data
      1. Representations for Complex Numbers
      2. Tagged Data
      3. Data-directed Programming and Additivity
    5. Systems with Generic Operations
      1. Generic Arithmetic Operations
      2. Combining Data of different Types
      3. Example: Symbolic Algebra


This is a draft; comments, bug reports, or proposals are welcome:



Machine Learning with Julia/Pluto.jl

1 Mathematical Background

1.1 Infinite Series

01 Geometric Sequence

02 Harmonic Series

03 Binomial Series

2 Stochastic Background

10 Boltzmann's and Shannon's Entropy

3 Machine Learning

3.1 Classification

20 Linear Binary Classifier


This is all draft for personal use. Comments or bug reports are welcome.

author: Prof. Dr. Claus Möbus, claus.moebus(at)


(Changed: 2021-10-31)