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 ||<|3> '''Weekly Sessions''' || '''Lecture 1''' || Monday, 14:00 - 15:30 ''(TEL 106, recommended)''||
 || '''Lecture 2''' || Monday, 15:30 - 17:00 ''(TEL 106, recommended)''||
 ||<|3> '''Weekly Sessions''' || '''Lecture 1''' || Monday, 14:00 - 15:30 ''(MAR 4033, recommended)''||
 || '''Lecture 2''' || Monday, 15:30 - 17:00 ''(MAR 4033, recommended)''||

Python Programming for Machine Learning (PyML)

Python Programming for Machine Learning (3 ECTS credits) is an optional course and part of the following modules:

In most cases, we do not recommend taking the course as a standalone. That is because the course is not a module (but part of a module), and most examination regulations only approve modules. However, there are exceptions, e.g., for some exchange students. Please carefully check your examination regulations in such a scenario.

Homework assignments must be completed by yourself and 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.

You can choose between two (almost identical) courses:

PyML A:

  • Course Period

    April 24th - May 19th 2023 (4 Weeks)

    Weekly Sessions

    Lecture 1

    Thursdays, 16:15 - 17:45 s.t. (online only, recommended)

    Lecture 2

    Friday, 09:00 - 10:30 s.t. (online, TEL106 available for streaming, recommended)

    Q&A / Exercise

    Friday, 10:30 - 12:00 s.t. (TEL 106, optional)

    Trainers

    Christopher Anders: anders [at] tu-berlin.de

    Panagiotis Tomer Karagiannis

  • Homework deadline: usually Mondays 23:55 (~10 days after the respective second lecture). Late submissions will not be graded!

PyML B:

  • Course Period

    June 5th - June 30th 2023 (4 Weeks)

    Weekly Sessions

    Lecture 1

    Monday, 14:00 - 15:30 (MAR 4033, recommended)

    Lecture 2

    Monday, 15:30 - 17:00 (MAR 4033, recommended)

    Q&A / Exercise

    Thursday, 16:00 - 17:30 (TEL 106, optional)

    Trainers

    Jannik Wolff: jannik.wolff@tu-berlin.de

    Panagiotis Tomer Karagiannis

  • Homework deadline: to be announced (at least one week after the respective second lecture). Late submissions will not be graded!

General information (valid for PyML A and B):

  • Language

    English

    Exams (choose one)

    June 9th, 12:30 - 02:30 p.m. (H 0105 Audimax)

    Jul 12th, 03:00 - 05:00 p.m. (H 0104)

    Jul 25th, 03:00 - 05:00 p.m. (A151)

    Links

    ISIS

    Wiki

Passing the course

  • The course is passed if the exam is passed (grade 4.0 or better) and all homeworks are completed successfully. (50/100 points or more, taking part in the exam requires successful completion of all homework)

  • The final course grade is determined by the exam only.

Taking the exam

  • You need to pass the PyML exam BEFORE taking the Cognitive Algorithms/ Machine Learning 2 exam in order to obtain the credits for this course. All of the PyML exams this semester are well before the registration deadlines of the respective courses, except if you choose to take the last exam on the 25th of July and plan to take the first Cognitive Algorithms exam on the 2nd of August, which has its registration deadline on the 26th of July, and its withdrawal deadline on the 31th of July, for which case we recommend to register for the CA exam as soon as possible, and then withdraw from the registration in case you failed the PyML exam.

  • For the PyML exams, you are allowed to take one A4 sheet of paper with handwritten text on both sides into the exam. Printed text is not allowed.

  • In case you fail the PyML exam, you can retry as many times as you like, however, you still need to pass the exam before taking the CA/ML-2 exam. Therefore, we recommend that you take the earliest PyML exam possible in order to prevent the completion of your module to be delayed until next semester/year.

Frequently Asked Questions (FAQ)

  • Where is the link for the class?

    • Links for the online lectures and exercises will be announced on ISIS just before they start.
  • Is prior programming/Python knowledge necessary?

    • Knowledge of elementary programming concepts (in Python or another language) will be helpful. Lack of such knowledge will increase the time demand of the class.
  • Which exam is compatible with PyML A/B?

    • All exams are compatible with PyML A or B. You can choose the exam that fits your time schedule best.
  • How to register for the course?

    • Registration via the exam registration office is not needed. However, you must enroll in the ISIS course to (a) submit the homework and (b) register for the exam.

  • 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 do not need to resubmit the homework. Please enroll in the ISIS course for the new semester and register for the exam on ISIS once possible (registration via the examination office is not possible/necessary).
  • Until when do I have to register at QISPOS? I do not have an ISIS account yet.

    • You have to register for a corresponding module, either ML or CA, until the deadline. Usually it is six weeks before the first exam.
  • Do I have to attend both exercise events every week or only one of them?

    • The exercise sessions are not mandatory. You can think of them as Q&A sessions, where you can ask questions regarding the homework.

  • I will not be able to attend a lecture. Is this a problem?

    • All learning materials will be made accessible online. You only need to submit your homework before the deadline. Ask in the ISIS forum if you have questions regarding lecture content.

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

    Python Programming for Machine Learning (KU)

    0434 L 543

    2

    (name of the trainer)

  • 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.

IDA Wiki: Main/SS23_PyML (last edited 2023-08-08 09:33:55 by JannikWolff)