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SUMMARY:Origins Data Science Block Courses
DTSTART;VALUE=DATE-TIME:20200901T110000Z
DTEND;VALUE=DATE-TIME:20200910T160000Z
DTSTAMP;VALUE=DATE-TIME:20200808T054523Z
UID:indico-event-4491@indico.ph.tum.de
DESCRIPTION:The Origins Data Science Lab (ODSL) is organizing two block co
urses of three afternoons each on data science topics.\n\nEach block consi
sts of six lectures of one hour\, followed by the possibility to work on a
set of problems\, including small calculations and implementations.\n\nTh
e courses will be online and we share the links to the registered particip
ants.\n\nBlock I (September 1-3): Introduction to Probabilistic Reasoni
ng\n\nIn this course we will introduce the basic concepts of reasoning und
er uncertainty. After a brief introduction to probability theory and commo
nly used probability distributions\, we discuss inference tasks with vari
ous probabilistic models. We conclude by outlining methods to approach mor
e involved inference tasks through approximation or sampling.\n\nLecturer:
Jakob Knollmüller (jakob.knollmueller@tum.de)\n\nPrerequisites: Linear A
lgebra\, basic Analysis\, a programming language of choice\n\nSkills acqui
red: basics of probabilistic reasoning and Bayesian inference\, probabilis
tic modelling\, model comparison\, approximate inference\n\nBlock II (Sept
ember 8-10): Introduction to Numerical Methods and Machine Learning\n\nThi
s course is focusing on methods for data processing\, optimization and mac
hine learning. First we will learn the basics of data decorrelation\, redu
ction and optimization algorithms. Based on these new skills\, we dive int
o machine learning topics\, such as clustering\, classification and regre
ssion with tree based algorithms and neural networks. In the last part dee
p learning models and different architectures will be introduced and expla
ined.\n\nLecturer: Dr. Philipp Eller (philipp.eller@tum.de)\n\nPrerequisit
es: Linear Algebra\, basic Analysis\, a programming language of choice\n\n
Skills acquired: basic data transformations\, knowledge in various optimiz
ation algorithms\, k-means clustering\, decision trees\, neural networks\,
convolutional neural networks\, auto-encoders\, generative models\n\nForm
s of credit\n\nIt is possible to get a Certification or ECTS points for pa
rticipation in the Block Courses:\n\nTo get a Certificate of Participation
(for either one of the two blocks or both)\, you will need to turn in sol
utions to the exercises that will be assigned during the course and get a
passing grade. The certification will be done on a course-by-course basi
s\, and will state that you have successfully completed the Block Course i
n 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 repor
ts.\n\nTo get the 3 ECTS points\, you will need to turn in solutions to th
e exercises for both Block Courses that will be offered this year. The gra
de for the course will be based on the two sets of exercises\, and there w
ill not be an additional exam. The deadline to hand in the report is Sept
ember 30\, 2020. Please register for the courses in advance so we can esti
mate how much work will be involved in the evaluation of the reports.\n\nh
ttps://indico.ph.tum.de/event/4491/
LOCATION:
URL:https://indico.ph.tum.de/event/4491/
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