**Classes**: Wed 9:45-11:00 (East Hall 4) and Fri 11:15-12:30, East Hall 8

**Tutorial session: **Tue 17:15-18:30, West Hall 4

**TAs**: Xu He (x.he at jacobs-university.de) and Tianlin Liu (t.liu at jacobs-university.de)

**Contents. **This course gives an introduction to the basic concepts of statistical modeling. We bring together the two views of statistics and of machine learning. While both traditions have developed advanced statistical tools to analyse data, the fundamental questions that are asked (and answered) differ. Stated briefly, statisticians try to *answer specific, decision-relevant questions* on the basis of data, whereas machine learners aim at *modeling complex pieces of the world in as accurately and comprehensively as possible*, given data. Both views are important in the current fast developments in "Big Data" or "Data Analytics". The course proceeds in four main parts: (i) the fundamental concepts of statistical modeling: probability spaces, observation spaces, random variables; (ii) a crash refresher on basic mathematical formulas and laws; (iii) introduction to statistical methods; (iv) introduction to methods of machine learning. The course was developed jointly by a statistician (A. Wilhelm) and a machine learner (H. Jaeger), and will be highly enriched by examples, exercises and miniprojects.

**Lecture notes**

- Part 1: Face to Face with Probability: Clear Concepts, Clean Notation (final (?) version 2.1 for this lecture, from Feb 17, 2018. Diff to version 2.0 from Feb 14: extensions in Section 7.3, 7.4)
- Parts 2 and 3: Introduction to Statistical Inference (outdated, will be entirely re-written)
- Part 4: Machine Learning in a Tiny Nutshell (version from Fall 2016)

**Homework. **There will be two kinds of homeworks, which are treated quite differently. **A. Paper-and-pencil problems**. These homeworks give an opportunity to exercise the theoretical concepts introduced in the lecture. These homeworks will not be checked or graded, and doing them is not mandatory. Instead, the problems will be discussed and show-solved in weekly tutorial sessions held by the TAs. Model solutions will be put online a week after issuing the problem sheets. **B. Programming miniprojects.** The other type of homework comes in the form of small-sized programming projects. Students work in teams of two or three, each team submitting a single solution, by email to the TAs, consisting of the code and a documentation (typeset pdf document, preferably generated in Latex, other word processing software allowed). These miniproject homeworks will be graded. Programming can be done in Matlab or Python.

**Grading. **The course grade will be computed from the following components: 1. three miniquizzes written in class (30 min) of which the best two will be taken and counting each by 20% toward the course grade; 2. classroom presence 10%; 3. programming homeworks 20%; 4. final exam 30%. All quizzes and exams are open-book.

**Schedule (to be filled in agreement with the unfolding of reality**

Feb 2 |
Introduction. |

Feb 7 | Lots of examples for probability measurement scenarios. Reading: Lecture Notes Part 1, Section 2 Exercise sheet 1 |

Feb 9 | Elementary events and random variables. Reading: LN Section 3 |

Feb 14 | Operations on RVs 1: products and projections Reading: LN Section 4.1 and Appendix A |

Feb 16 | Operations on RVs 2: transformations of RVs. Modeling time series data by RVs. Reading: LN Sections 4.2 and 5. Exercise sheet 2 |

Feb 21 | Events and sigma-fields. Reading: LN Section 7.1, 7.2 up to (excluding) Theorem 3. |

Feb 23 | More on sigma-fields. The Borel sigma-field. Generating sigma-fields. Reading: LN 7, to its end. Exercise sheet 3 |

Feb 28 | |

Mar 2 | |

Mar 7 | |

Mar 9 | |

Mar 14 | |

Mar 16 | |

Mar 21 | |

Mar 23 | |

Apr 4 | |

Apr 6 | |

Apr 11 | |

Apr 13 | |

Apr 18 | |

Apr 20 | |

Apr 25 | |

Apr 27 | |

May 2 | |

May 4 | |

May 9 | |

May 11 | |

May 16 | |