Allgemeines zum Kurs
The course is designed to enhance your learning experience.
Each unit is motivated by a story based problem and focussed on providing the skills and techniques to solve it.
Be part of Captain Bayes' crew to sail the ocean of uncertainties and big data and help to succeed in her adventures of probability theory.
Get to know the ideas of your fellow crew comrades Bernoulli, Pascal, Laplace and many more that accompany you during your journey.
The adventures of Captain Bayes are followed by the content videos that deliver the theories and concepts of each unit. To familiarize you with the new learnings, interactive quizzes are integrated in the video to deepen your knowledge practically.
Furthermore listen to Ernesto, the parrot, that will guide you as your learning buddy throughout the course and provide you with learning tips and further material.
At the end of each unit you are ready to help Captain Bayes to solve the raised problems in a self-assessment (5-10 questions).
Hire now, meet Captain Bayes and proof yourself in her tough initial / recruiting task under the following link.
The primary motivation for this course is to warm a broader audience to probability theory, as it is central to all scientific fields. Or as E.T. Jaynes - one of the most important researchers on probability theory of the last century - put it: "Probability is the logic of science."
This course equips you with the methods of probability theory and enables you to deal with uncertainties, qualify decisions, assign probabilities and estimate parameters and models. The Bayesian approach provides techniques to update "partial truth".
As a whole, this MOOC delivers the basics that are needed and benefitial for the fields of machine learning and data science.
The course is split into units dealing with discrete and continuous variables with 9 units altogether:
- Unit 1: Bayesics of probability theory
- Unit 2: Discrete probability distributions and samples
- Unit 3: Multivariate distributions - more Bayesics
- Unit 4: Combinatorics - The art of counting
- Unit 5: Odyssey and stochastic processes
- Unit 6: Bayesian deep reasoning
- Unit 7: Parameter and model estimation and classification
- Unit 8: Continuous probability distributions and invariance
- Unit 9: Bayesian simulation techniques
There will be a break of two week between unit 4 and 5 due to Easter holidays.
The ambition of this course is not only to deliver the concepts of probability theory but to promote critical questioning and to sensitize for decision making.
Dozens of paradoxa show how easily the human mind can be misled. Therefore, the skill to interprete numbers and facts is as important as the ability to make qualified estimations. The course will equip you with the toolbox to draw inference based on uncertain and incomplete information.
Specifically the course will help you to understand how to assign probabilities in a classical and statistical way and more importantly how to use new evidence to update probabilities and solve inverse probability problems (Bayes theorem).
Especially rigorous updating and solving inverse problems are fundamental for machine learning and data science and some examples in the course will show how to estimate parameters and classify data.
Since correct data processing is crucial for knowledge gain by experiments, dealing with outliers and the rigourous drawing of conclusions from sample sets will be topics of this course.
After completing this course you will be enabled to formulate probabilistic statements from (new) data and to use simulations to draw conclusions for stochastic processes.
From a didactical perspective a major goal is to improve discussion culture among participants, to help you to express your ideas and your questions and to foster your engagement.
The first seven modules deal with basic algebra. The content is designed for Bachelor students with a basic mathematical backround. For unit 1 to 7 the sum notation, vector calculus and simple algebraic transformation should be known. The last units about continuous variables require integration techniques.
Lecture at TU Graz / Uni Graz:
Wahrscheinlichkeitstheorie, Statistik und Datenanalyse - (PHY.E50UF, PHY.E50UB)
Please register for this MOOC with your eduID in any case. This is absolutely necessary for the issue of a certificate (confirmation of participation).
For further details, please refer to the course description or your curriculum itself, or contact the lecturer.
For actively participating in the course you will receive an automatic
confirmation of participation (certificate) which includes your
username, the course title, course duration as well as the hours
required to complete the course. We want to point out that this
certificate merely confirms that the user answered at least 75% of the
self-assessment questions correctly.
This work is licensed under a CC-BY-4.0
Wolfgang von der Linden is a full professor and head of the institute of Theoretical and Computational Physics at TU Graz. His research focuses on strongly correlated many body physics and Bayesian probability theory which in 30 years led to more than 100 papers on this topic. He has collected his collective experience in the book "Bayesian probability theory: applications in the physical sciences." Cambridge University Press, 2014.
Gerhard Dorn is a project assistant at the Institute of Theoretical and Computational Physics and engaged in teaching for over ten years.
Besides his physics research in quantum transport he has a strong mathematical background and is enthusiastic about story telling in science communication.
Team Pixel Art:
Gihan El Moazen is a bioengineering student with a faible for pixel art and graphical design.
Annika Schnell is a physics and informatics student who is passionate about game development and pixel art.
Creative Story and content development team:
Tristan Feyer, a physics student, brings in his motivation for story development, writing dialogue and drafting sceneries.
Gloria Wolkerstorfer is finishing her master studies in theoretical physics with a focus on Bayesian probability and like Tristan, is part of the creative story and content development team.
Interactive simulation and voice acting:
Johanna Moser is a physics student who contributes to developing interative simulations and related stories.
Michael Sieberer is a PhD student in physics who helps realizing the solution for interactive simulations.
Christian Gruber, Florian Payerl and Jakob Hinum-Wagner are part of the voice acting team supported by our vocal coach Reinhard Hütter.
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Trailer zu Bayesian probability theory
Technische Universität Graz