EVA: Complementary Modules 2019-20

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
Title: Machine Intelligence Lab
Short Code: EVA_MILab
ECTS Credits: 4
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: 31 August 2019 
Start Date: 16 September 2019 
End Date: 20 December 2019 
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:
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 (as documented by your earned certificate). You pass this module if your final presentation of the hackathon demonstrates reasonable application of the taught skills from part 1.

Contact:

Frank-Peter Schilling (scik@zhaw.ch)
Thilo Stadelmann (stdm@zhaw.ch)

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

 

[Responsible for this text: Frank-Peter Schilling]