Origins Data Science Block Courses

Europe/Berlin
Description

The Origins Data Science Lab (ODSL) is organizing two block courses of three afternoons each on data science topics.

Every block will take place on the afternoons from Tuesday to Thursday with the following schedule:

  • Lecture 13:30-14:30
  • Tutorial 14:30-15:30
  • Break 15:30-16:00
  • Lecture 16:00-17:00
  • Tutorial 17:00-18:00

The courses, as well as the tutorials, will take place online. The tutorials will be organised through breakout rooms, each assigned to a tutor.

The course itself, as well as one tutorial will be recorded, the other tutorials not. If you have any objections to this, please contact us directly. We will make the recordings of the lectures available online until the deadline for turning in reports (see below). After that date, the recordings will be removed and no longer be accessible.

All exercises will be made available before the courses and the deadline to hand in the report is September 30, 2020.

The link to the zoom meeting for the block course is:

https://tum-conf.zoom.us/j/95071255523 (Password: 091395)

Also, the recordings of the lectures and tutorial sessions will be made available at:

https://tum.cloud.panopto.eu/Panopto/Pages/Sessions/List.aspx?folderID=baec0005-d578-486d-a315-ac2a009ce665

 

Block I (September 1-3):  Introduction to Probabilistic Reasoning

In this course we will introduce the basic concepts of reasoning under uncertainty. After a brief introduction to probability theory and commonly used probability distributions, we discuss inference tasks with various probabilistic models. We conclude by outlining methods to approach more involved inference tasks through approximation or sampling.

Lecturer: Jakob Knollmüller (jakob.knollmueller@tum.de)

Prerequisites: Linear Algebra, basic Analysis, a programming language of choice

Skills acquired: basics of probabilistic reasoning and Bayesian inference, probabilistic modelling, model comparison, approximate inference

Block II (September 8-10): Introduction to Numerical Methods and Machine Learning

This course is focusing on methods for data processing, optimization and machine learning. First we will learn the basics of data decorrelation, reduction and optimization algorithms. Based on these new skills, we dive into machine learning topics, such as clustering, classification and regression with tree based algorithms and neural networks. In the last part deep learning models and different architectures will be introduced and explained.

Lecturer: Dr. Philipp Eller (philipp.eller@tum.de)

Prerequisites: Linear Algebra, basic Analysis, a programming language of choice

Skills acquired: basic data transformations, knowledge in various optimization algorithms, k-means clustering, decision trees, neural networks, convolutional neural networks, auto-encoders, generative models

Forms of credit

It is possible to get a Certification or ECTS points for participation in the Block Courses:

To get a Certificate of Participation (for either one of the two blocks or both), you will need to turn in solutions to the exercises that will be assigned during the course (tutoring session exercises) and get a passing grade.  The certification will be done on a course-by-course basis, and will state that you have successfully completed the Block Course in the respective topic.  Please register for the course in advance so we can estimate how much work will be involved in the evaluation of the reports.

To get the 3 ECTS points, you will need to turn in solutions to all exercises for both Block Courses (tutoring + homework exercises) that will be offered this year. The grade for the course will be based on the two sets of exercises, and there will not be an additional exam. The deadline to hand in the report is September 30, 2020. Please register for the courses in advance so we can estimate how much work will be involved in the evaluation of the reports.

Participants who do not want a certificate or points are not required to turn in solutions, but are allowed to.

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