Learning and Cognitive Systems

Motivation

In this set of exercises we want to study the suitability of PPLs for Probabilistic Functional Reactive Programming. We reuse the instructions of programming assignments provided by Sebastian Thrun in his Udacity lecture 'AI for Robotics'. In those assignments students have to complete Python code templates. Here, we offer solutions in various probabilistic programming languages (PPLs). In contrast to Thrun's conventional Python-based approach we follow a strict functional and sampling guided approach when implementing code. First, we start here with WebChurch. The signature of all functions is contained in this cheatsheet.

Copyright Notice

We asked Udacity for a permission to use the Instructions and some slides of Sebastian Thrun's lecture "AI for Robotics". We got this permission per email on 2016/04/19:

"Our content is available under Creative Commons non-commericial no-derivatives 3.0 license. The short summary is that:

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  • You may not use our content for commercial purposes

  • You may not alter, transform, or build upon our content, apart from contributing to our open translation of subtitles for foreign language users."

    Zhalisa Clarke

    VP, Business Development

    Udacity featured in Fortune and the NYTimes