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SUMMARY:IMPRS-EPPC Hands-on Block Course: Fundamentals of Machine Learning
DTSTART:20260323T080000Z
DTEND:20260326T160000Z
DTSTAMP:20260911T130800Z
UID:indico-event-177@indico.fhi-berlin.mpg.de
CONTACT:imprs@fritz-haber-institut.de\;alexander.paarmann@fhi-berlin.mpg.d
 e
DESCRIPTION:IMPRS-EPPC spring block course 2026Fundamentals of Machine Lea
 rning - Hands-On CourseWhen:  Mar 23 -26\, 2026Where: Fritz-Haber-Institu
 t\, Berlin             DirectionsThis course is primarily provided 
 to members of the IMPRS-EPPC. Registration by invitation only. Course req
 uirements:Own laptop\, Python pre-installed (instructions)Each participant
  is required to attend the full course Mo-Th\, Python foundations and mach
 ine learning workshop.Part 1: Python FoundationsHands-on course led by T. 
 Melson\, N. Horlava\, P. Coronica (MPCDF)Mo/Tu\, Mar 23/24\, FHI Buiding P
 \, Seminar Room P 2.05\, 9:00-17:00Link for materialsCourse Description: 
 The course walks participants through the entire lifecycle of a Python pro
 ject\, from a single-script prototype to a fully version-controlled\, test
 ed\, and publishable package ready to be developed collaboratively. Short\
 , focused lectures alternate with practical\, guided coding sessions to en
 sure that participants can immediately apply what they have just learned. 
 While the hands-on exercises use Python\, the core concepts (project struc
 ture\, version control\, testing strategies\, CI/CD\, documentation\, etc.
 ) are language-agnostic and transferable to other ecosystems.Social event:
 Tu\, Mar 24\, 17:00\, Richard-Willstätter-Haus (FHI\, building M)Part 2: 
 Machine Learning & AutomationHands-on course\, group work\, lead by C. Sc
 heurer (FHI Theory)\, Mentored by G. Ducci\, C. Kunkel\, S. Rejman\, M. D
 eimel\, D. Balaz\, S. Fürst\, C. Pare\, M. Vuijik\, M. Kouyate (FHI Theor
 y).We/Th\, Mar 25/26\, FHI seminar room building M & other rooms (see sche
 dule)\, 9:00-17:00Course description:The course will cover various classes
  of machine learning (ML) concepts as well as basics of lab-automation. We
  will give short intros to three classes of ML algorithms (e.g. NNs\, symb
 olic regression\, GPR\, etc.) as well as fundamentals of lab automation. 
 The ML segment will cover typical application areas\, simple math backgrou
 nd of prototypical algorithms/models\, and well-known toolboxes. The lab a
 utomation segment will focus on interfacing typical lab instruments with P
 ython\, implementing small use cases\, and learning common protocols to in
 terface with such hardware. The students must then decide which of the tw
 o segments they want to get their hands on during group work. For the mach
 ine learning part\, students will form groups of 6. For the lab automation
  part\, students will form smaller groups of 2-3 people. Each team will be
  posed a simple ML problem for their method or a practical hardware task t
 hat they need to implement. TAs will provide support if needed. On Thu af
 ternoon\, each team will present the findings on their chosen track to the
  other teams\, discussing the different aspects of how they solved their p
 roblem and what they learned along the way. \n\nhttps://indico.fhi-berlin
 .mpg.de/event/177/
LOCATION:Fritz-Haber-Institut
URL:https://indico.fhi-berlin.mpg.de/event/177/
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