In the age of Big Data, Data Science, Machine Learning and Artificial Intelligence, programming with probabilities is a condicio sine qua non. And indeed the number of useful tools and libraries are legion. Disciples in these disciplines usually resort to R, Octave, Python - you name it - to do data analysis. And mastering these tools and libraries will probably land you a high-paying job. However, there is a serious drawback progressing this way - you will have a very hard time understanding how these tools and libraries do what they do and changing what they do remains a painful exercise, more painful that it need be.
Probabilistic functional programming allows developers to program with probabilities in a high-level language that is:
- close to the mathematical concepts one intends to program,
- easy to interpret and to visualize results.
Hence, probabilistic functional programming makes the obstacle of learning how to program with probabilities a lower one, it focuses on the concepts rather than on syntax and it avoids tool-ism, which is the blind-folded approach of calling up something one does not understand entirely - to some extent.
Given the current landscape of tools and libraries, when you become a die-hard-data scientist you will probably not be able to use current day probabilistic programming language like Church a lot. However, you will luckily remember the elegance of the solutions you have seen and truely understand what you have to accomplish.
Lecture and Tutorial - Probabilistic Functional Programming (6 ECTS; for Master and Bachelor students in Web Science, Computer Science, Computational Visualistics and Business Informatics)
Students of computer science can use this course for Theory or for Data and Knowledge Engineering
|Dozent(in)||Prof. Dr. Steffen Staab|
Fr 10.15-11.45, Room F413
|Dozent(in)||Dr. Mahdi Bohlouli|
Th 12.15-13.45, Room E524
Lectures are hold on Fridays beginning October 28, practical exercises are hold on Thursdays beginning November 3.
Exam is targeted to be hold on Monday, Feb 20, 2017 at 10:00 AM in room M 201.
|Date||Lecture Topics (to be revised)||Slides|
Motivation and applications
Functional Programming with Scheme (1)
Functions, lists, let, lambda abstraction, application, recursion
Functional Programming with Scheme (2)
Tail recursion, closures
|24.11||Generative models - Part 2||GenerativeModelsPart2.pdf|
|8.12.||Patterns of Inference||Patterns of Inference.pdf|
|Models for sequences of operations||Sequences of Observations.pdf (updated Dec 15)|
|15.12.||Inference about inference||InferenceAboutInference.pdf|
|13.1.||Algorithms for inference||MCMC.pdf|
|20.1.||Algorithms for inference - Part 2||MetropolisHastings.pdf|
|27.1.||Learning as conditional inference
|10.2.||- cancelled -|
|17.2.||Finale||update slides of OccamsRazor|
|Date||Lecture Topics (to be revised)||Slides|
Getting started with Church
|10.11.||Practicing Church and basic instructions||practicing_Church_part1.pdf|
|17.11.||Practicing Church and Functions||practicing_Church_part2.pdf|
|25.11.||Discussion on Assignment 1 & further examples||Assignment_Solutions.pdf|
|15.12.||Discussion on Assignment 2 & further examples||Assignment02_Solutions.pdf|
|12.01.||Discussion on Assignment 3 & further examples||Assignment03_Solutions.pdf|
|19.01.||Discussion on Assignment 4 & further examples||Assignment04_Solutions.pdf|
|26.01.||Discussion on practical examples|
|02.02.||Discussion on Assignment 5 & further examples (updated: 14th February)||Assignment05_Solutions.pdf|
|09.02.||Discussion on practical examples|
|16.02.||Further examples and review||Review.pdf|
|Release Date||Assignment (to be revised)||Submission Deadline|
|09.11.||Assignment Number 01 (20 Points)||23.11|
|02.12.||Assignment Number 02 (20 Points)||09.12|
|13.12.||Assignment Number 03 (14 Points)
(updated 22 December)
|12.01.||Assignment Number 04 (20 Points)||17.01.|
|26.01.||Assignment Number 05 (20 Points)||30.01.|
|14.02.||Assignment Number 06 (20 Points)||17.02.|
Core Literature & Systems
- Abelson, Sussman: Structure and Interpretation of Computer Programs, MIT Press, http://groups.csail.mit.edu/mac/classes/6.001/abelson-sussman-lectures/
- N. D. Goodman and J. B. Tenenbaum (electronic). Probabilistic Models of Cognition. Retrieved June 20, 2016 from http://probmods.org.
Further Literature & Systems
- Wood, F., van de Meent, J. W., & Mansinghka, V. (2014). A New Approach to Probabilistic Programming Inference. In Proceedings of the 17th International conference on Artificial Intelligence and Statistics (1024-1032). BIB PDF
Michael Izbicki. HLearn: A Machine Learning Library for Haskell. Retrieved June 20, 2016.
- Extended WebChurch reference summary, WebChurch http://rpubs.com/CMoebus/123012.
- Probabilistic (Bayesian) Programming, University of Oldenburg, https://www.uni-oldenburg.de/en/computingscience/lcs/probabilistic-programming/