Julia Programming for Machine Learning (JuML)

Julia Programming for Machine Learning (3 ECTS credits) is an optional course within one of the modules:

• "Cognitive Algorithms" (summer and winter semesters)

• "Machine Learning 1" (winter semester).

• "Machine Learning 2" (summer semester).

It's not possible to take the class as a standalone, seminar, or free-of-choice module.

Homework assignments must be submitted every week. You must be enrolled on ISIS to submit homework. If you do not register on time, you cannot pass the course.

• Course Period	April 26th - May 24th 2023
  Lecture	Wednesday 09:15 - 11:45 PM (virtual, Zoom link on ISIS), PCs available in TEL 106
  Office hour	Monday 14:15 – 15:45 (virtual, Zoom link on ISIS)
  Language	English
  Trainer	Adrian Hill: hill [at] tu-berlin.de
  ISIS	Updated Link
  Exams (choose one)	June 9th, 12:30 - 02:30 PM
  	TBD

Frequently Asked Questions (FAQ)

• Which exam is compatible with JuML? All exams are compatible with JuML. You can choose the exam that fits your time schedule best.

• Is prior programming/Julia knowledge necessary? Knowledge of elementary programming concepts (in Julia or another language) will be helpful. Lack of such knowledge will increase the time demand of the class.

• How to register for the course? Pre-registration via the exam registration office is not needed. However, register for the ISIS course in time (see above) to be able to submit the exercises.

• May I participate in the class during this semester and take part in the corresponding module in one of the following semesters ? Yes, just after you've passed the class, your results are also valid for the next semesters.

• I've already successfully passed all of the homework in the previous semester but failed/missed the exam. Should I resubmit them again? No, you don't need to resubmit the homework. You may take part directly in the final exam. Enroll for the class via ISIS and wait for the announcements.

Students from other universities

If you are not a student at TU and want to earn credit, you have to solicit ''Nebenhörerschaft'':

• First fill the online ''form'' with the following details on the final page.

  Course title and type	Program number	Number of course hours per week (SWS)	Lecturer
  Julia Programming for Machine Learning	0434 L 543	2	Adrian Hill

• Send the form to one of the trainers (see contact information above) to sign
• Then send the signed form to Manuela Gadow (manuela.gadow at tu-berlin.de), who is authorized to sign on behalf of our dean.

• After getting your signed form back send it together with your current matriculation letter ('Immatrikulationsbescheinigung') to the student registration office (nebenhoerer@studsek.tu-berlin.de).

Please make sure to trigger the above process on time. You may need to go through some bureaucracy to attain ISIS (the university's web portal) access, which is necessary to submit the mandatory homework.