Rafael Polania: Catalogue data in Spring Semester 2023 |
| Name | Dr. Rafael Polania |
| Address | Neurowiss. der Entscheid.prozesse ETH Zürich, Y36 M 6 Winterthurerstrasse 190 8057 Zürich SWITZERLAND |
| Telephone | +41 44 633 99 75 |
| Department | Health Sciences and Technology |
| Relationship | Assistant Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 376-1986-00L | Bayesian Data Analysis and Models of Behavior (University of Zurich) Does not take place this semester. No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student. UZH Module Code: DOEC0829 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html | 3 credits | 2S | R. Polania, University lecturers | |
| Abstract | Making sense of the data acquired via experiments is fundamental in many fields of sciences. This course is designed for students/researchers who want to gain practical experience with data analysis based on Bayesian inference. Coursework involves practical demonstrations and discussion of solutions for data analysis problems. No advanced knowledge of statistics and probability is required. | ||||
| Learning objective | The overall goal of this course it that the students are able to develop both analytic and problem-solving skills that will serve to draw reasonable inferences from observations. The first objective is to make the participants familiar with the conceptual framework of Bayesian data analysis. The second goal is to introduce the ideas of modern Bayesian data analysis, including techniques such as Markov chain Monte Carlo (MCMC) techniques, alongside the introduction of programming tools that facilitate the creation of any Bayesian inference model. Throughout the course, this will involve practical demonstrations with example datasets, homework, and discussions that should convince the participants of this course that it is possible to make inference and understand the data acquired from the experiments that they usually obtain in their own research (starting from simple linear regressions all the way up to more complex models with hierarchical structures and dependencies). After working through this course, the participants should be able to build their own inference models in order to interpret meaningfully their own data. | ||||
| Prerequisites / Notice | The very basics (or at least intuition) of programming in either Matlab or R | ||||