EVA: Complementary Modules 2022-23

Opened: Saturday, 2 December 2017, 11:20 AM

Complementary modules organized by all institutions participating in the MSE.

Please note:

  • The number of inscriptions is typically restricted
  • Please consider the status field: only modules with "registration open" status can be booked
  • Module inscriptions have to be made via your advisor to the contact person as specified in the offering
Lecturers who want to offer and edit EVA:
  1. Login to Moodle (Link)
  2. Return to this page (Link)
  3. Now you should see your entries (which may not be released and publicly accessible yet)
  4. By pressing the edit symbol (gear wheel) at the very bottom, an entry can be edited
  5. For creating new module descriptions or in case of problems: please e-mail to Michael Röthlin (michael.roethlin@bfh.ch)
  6. The respective UAS are responsible for providing Moodle courses for the EVA listed here; such courses will not be provided on the MSE Moodle installation!
Thank you very much for your cooperation!
Title: Machine Intelligence Lab
Short Code: EVA_MILab
ECTS Credits: 3
UAS: ZHAW
Organizer Details: ZHAW Zurich University of Applied Sciences, School of Engineering, Institute for Applied Information Technologies (InIT), Winterthur, Switzerland
Evaluation:
  • Successful completion of MOOC
  • Successful participation in Hackathon with final presentation
Decision Date: 2 September 2022 
Start Date: 19 September 2022 
End Date: 27 January 2023 
Date Details:

Part 1: You successfully complete a public MOOC in the area of machine intelligence of ca. 12 weeks duration, including solving all lab assignments needed to pass. You will be mentored in ca. bi-weekly colloquia by your ZHAW lecturers. The first part is finished with the (free-of-charge) certification of successful graduation from the MOOC provider.


Part 2: You undertake (single or in a small group) a one week hackathon: You will be given a machine intelligence project by your lecturers (problem description, data) at the morning of the first hackathon day. On the evening of the final day, you give a presentation on your proof of concept implementation with an outlook for future work.

Type:

Seven biweekly discussion sessions of one hour plus two sessions of final project presentations and discussions (in-class teaching discussion and presentations; total 15 hours) plus independent self-learning and directed workgroup (total: 75 hours)

Language(s):

English

Description (max. 300 characters):

You complete a public MOOC in the area of machine intelligence, guided by your ZHAW lecturers. After successful completion, you put your acquired skills to the test in a one-week hackathon. This way you gain broad application know-how in a specialized area of machine learning.

 

 

Contents and Learning Objectives:

You gain skills in a selected machine learning area besides what is covered in central modules:

  • You gain theoretical understanding of the methods and test it practical programming exercises
  • You are able to apply your skills properly and targeted in machine intelligence projects
  • Thus, you are able to assess and demonstrate the feasibility of project ideas
Admission: Undergraduate level skills in programming, linear algebra, probability theory, descriptive statistics
Literature:

As common machine learning and deep learning is well covered in the MSE curriculum at the moment, we will likely look into some specialized topics such as reinforcement learning, e.g.

 
Conditions:

You are admitted to part 2 if and only if you have solved successfully all labs/programming exercises for part 1 of the course. You pass this module if your final presentation of the hackathon demonstrates reasonable application of the taught skills from part 1.

Contact:

Ricardo Chavarriaga (char@zhaw.ch)
Thilo Stadelmann (stdm@zhaw.ch)

 
Contact Person E-Mail: Ricardo.chavarriaga@zhaw.ch
Status: registration open
 
Specialization: Information and Communication Technologies (ICT)

Computer Science (CS)

Data Science (DS)

 

[Responsible for this text: Chavarriaga Ricardo]