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Principles of Statistical Modeling Spring 2018 (340101)

Jacobs University Bremen, Spring 2018, Herbert Jaeger

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 and Tianlin Liu (t.liu at

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 are here. This is a new (version 0.1, April 17) version which combines in one manuscript the parts that were distributed as separate documents before.

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

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 7.2, to its end. Exercise sheet 3
Feb 28 Measurable functions. Observing structure through the structure of observations. Reading: LN 7, complete
Mar 2 The full picture: probability spaces. Notation: how to correctly write down probability statements. Reading: LN Section 8. Exercise sheet 4
Mar 7 Conditional probability.   Exercise sheet 5
Mar 9 no class
Mar 14 no class
Mar 15  miniquiz 1. 19:00, CNLH
Mar 16 Bayes' formula. Samples. Reading: LN Section 9.  Partial solution for exercises 5. Exercise sheet 6
Mar 21 Estimators. Distributions. Representing distributions. Marginals. Reading: LN Section 9 to end, LN Section 10
Mar 23 Expectation, variance, covariance, moments. Reading: LN Section 11 Partial solution for exercises 6 Exercise sheet 7
Apr 4 Independence. Markov Chains I. Reading: LN Sections 12 and13.  Solutions to sheet 7 Exercise sheet 8
Apr 6 Markov Chains II. A glimpse on hidden Markov models.
Apr 11 A glimpse on Bayesian model estimation.  Reading: LN Section 16 (we skip Sections 14 and 15).
Apr 13 Some widely used distributions. Solutions to sheet 8 Exercise sheet 9  an old final exam Reading: LN Section 17
Apr 18 Uses of probability theory in the natural sciences and signal processing & control.
Apr 19 miniquiz 2. 19:00, CNLH
Apr 20 Quiz outcome - discussion. Solutions to sheet 9. Exercise sheet 10
Apr 25 Part II: statistics. Introduction.  Statistics: formalization of the statistical problem.
Apr 27  
May 2  
May 4  
May 9  
May 11  
May 16  
May 22 12:30 - 14:30 final exam CNLH (pre-announcement, needs confirmation)