| 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)
|
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