Author: Iye Szin Ang

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Enrollment as a student

Iye Szin Ang wiki_phds

This post is intention to help future international PhD students to ease their enrollment process.

Step 1: Find a mentor and prepare your documents

You need to find a topic, working title, individual curriculum and supervising team (supervisor and mentor) yourself to be able to fill out the form. According to our curriulum for the doctoral studies the mentor needs to have a venia docendi. 

If your degrees are not from a EU country, you need a full authentication or an Apostille of your documents (and the translations, if your documents are not in English), please inform yourself if you’d need either an authentication or Apostille for Austria! This is valid for both, Master’s documents as well as bachelor’s!

If you can not obtain an authentication/Apostille we also accept a translation of your 4 academical documents by an officially licenced translator in Austria instead.

Step 2: Applications form

You have also required to fulfil 20 ECTS, they have to be split in main and secondary subject:

  • Courses that don’t have anything to do with your subjects (language courses etc.) you can do up to 4 ECTS

  • Privatissima (talking to your prof, SE type) for a maximum of 4 ECTS

  • There is no limit on IV courses

  • And you can also have stuff (eg. summer school) from other unis, however you have to go through the equality process (they will check if it really is equal to the ects here)

These stated in the curriculum, which is in German.

Example course:

 

Step 3: Online registration for graduates of Non-Austrian University

Create a basic account in MUonline of the university. You have to create a student account even you have a staff account.

p/s: Sign out your staff account when you’re in the process of creating a basic account.

Detailed admission process at here.

Step 4: Waiting and make appointment to verify the real document

190.015 Applied Machine and Deep Learning (5SH IL, WS)

Iye Szin Ang teaching_currentlectures

Course Content

In the first week, advanced machine and deep learning methods like multi-layer-perceptrons, convolutional neural networks, variational autoencoder, transformers, simultaneous navigation and mapping approaches, and more will be presented.

These methods can be tested using interactive tools like for example using   https://playground.tensorflow.org. To deepen the knowledge, students will answer well-crafted scientific questions using latex handouts alone or in teams of two students in the lecture room. 

Additionally, Jupyter notebook files were prepared to implement advanced machine and deep learning approaches without installing any software. For all participants of the course user accounts will be created using our JupyterHub at https://jupyter.cps.unileoben.ac.at. The accounts will remain active till the end of the semester. 

Prerequisites & If you Miss Course Contents

During the first week, a laptop or tablet will be needed to use the interactive tools and the Jupyter notebooks. 

Webex Online Sessions of the 1st Week

Find here the link to the online stream during the first week in October, 2024: https://unileoben.webex.com/unileoben/j.php?MTID=m5492385776dd885ca5dde72e52563c61

When you miss some course contens

If you miss some course contents due to overlapping events, you can watch recordings of the sessions online. All recordings will be hosted via Moodle at https://moodle.unileoben.ac.at/course/view.php?id=3082.

 

Course Description

Modern machine learning methods and in particular deep learning methods are entering almost all areas of engineering. 

The integrated course enables the students to apply these methods in the application domains of their study.

For this purpose, current problems from the industry are investigated and the possibilities of machine and deep learning methods are tested.

Students gain a deep understanding of method implementations, how data must be prepared, which criteria are relevant for selecting learning methods, and how evaluations must be performed in order to interpret the results in a meaningful way.

Initially, the basics of learning methods are developed in 5-6 lecture units. Then, students select one of the listed industrial problems and work on it alone or as a team (with extended assignments). The project work is accompanied by weekly tutorials with tips and tricks. Finally, the project results are discussed in a written report and presented for a final 10-15min.

Grading is based on the quality of the code, the report, and the final short presentation.

Among others, one of the following industry problems can be chosen:

1. Application and comparison of deep neural networks for steel quality prediction in continuous casting plants with data from the ‘Stahl- und Walzwerk Marienhütte GmbH Graz’.

2. Predictive maintenance of bearing shells using frequency analysis in decision trees and deep neural networks based on acoustic measurement data.

3. Motion analysis and path planning for human-machine interaction in logistics tasks with mobile robots of the Chair of CPS.

4. Autonomous navigation and mapping with RGB-D cameras of the four-legged robot Unitree Go1 for excavation inspection in mining.

The project list is continuously extended.

Links and Resources

Location & Time

  • Location: HS 3 Studienzentrum
  • Dates: 01.10.2024 – 07.10.2024, see the course schedule above.
  • Location: Digital Science Center (Roseggerstraße 11, 8700 Leoben)
  • Date: 22.01.2025, 10:15 – 15:15, final presentation
 

Kickoff meeting of project

All meetings will be conducted at CPS chair. The time please refer to the email, contact us if reschedule is needed.

Previous Knowledge Expected

Formal Prerequisite: Basic Python programming skills, Fundamentals of Statistics.

Recommended Prerequisites: Introduction to Machine Learning (“190.012” and “190.013”).

Slides

Learning objectives / qualifications

  • Implement or independently adapt modern machine learning methods and in particular deep learning methods in Python.

  • Analyze data of complex industrial problems, process (filter) the data, and divide it into training- and test data sets such that a meaningful interpretation is possible.
  • Define criteria and metrics to evaluate evaluations and predictions and generate statistics.

  • Develop, evaluate, and discuss meaningful experiments and evaluations.

  • Identify and describe assumptions, problems, and ideas for improvement of practical learning problems.

Grading

Continuous assessment: During the lectures and the tutorials 0-20 bonus points can be collected through active participation.

Project assignments: Alone or in small groups (2-3 students) one of the listed projects has to be implemented. A written report in form of a git repository wiki page have to be submitted.
– For the implementation (Python Code) 0-40 Points can be obtained.
– For the wiki page report, 0-60 Points will be given.

Grading scheme: 0-49,9 Points (5), 50-65,9 Points (4), 66-79 Points (3), 80-91 Points (2), 92-100 Points (1).

With an overall score of up to 79%, an additional oral performance review MAY (!) also be required if the positive performance record is not clear. In this case, you will be informed as soon as the grades are released. If you have already received a grade via MU online, you will not be invited to another oral performance review.

Literature

Machine Learning and Data-modelling:

– Rueckert Elmar 2022. An Introduction to Probabilistic Machine Learning, https://cloud.cps.unileoben.ac.at/index.php/s/iDztK2ByLCLxWZA

– James-A. Goulet 2020. Probabilistic Machine Learning for Civil Engineers. MIT Press.

– Bishop 2006. Pattern Recognition and Machine Learning, Springer.

Learning method Programming in Python:

– Sebastian Raschka, YuxiH. Liu and Vahid Mirjalili 2022. Machine Learning with PyTorch and Scikit- Learn. Packt Publishing Ltd, UK.

– Matthieu Deru and Alassane Ndiaye 2020. Deep Learning mit TensorFlow, Keras und TensorFlow.js., Rheinwerk-verlag, DE. 

Problem specific Literature:

– B. Siciliano, L.Sciavicco 2009. Robotics: Modelling, Planning and Control, Springer.

– Kevin M. Lynch and FrankC. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.

– E.T. Turkogan 1996. Fundamentals of Steelmaking. Maney Publishing,UK.

After Business Trip Paperwork

Iye Szin Ang wiki_phds

New Obligation: Submit along comparitive offers

(This starts from 30 July 2024)

You are required to submit a comparative analysis of the prices for your trip along with other documents for claims.

Documents to submit and print in hard copy:

  • Conference/summer school schedule
  • Transport ticket (flight/intercity train/city train/bus)
  • Registration fee
  • Spesenabrechnung/Reisekostennachweis (from SAP)
  • Accomodation
  • Comparative analysis of the prices (only be paid 50% of the flight costs, if this document is not submitted along)

New Obligation: Monthly data entry for öbb tickets

(This starts from 26 Jun 2024)

You can find the entry form at here: https://cloud.cps.unileoben.ac.at/index.php/s/GTFTrT8btK7mMtW

Procedure to submit paperwork to Financial Department

Published on 21 May 2024

Update 1 on 26 Jun 2024

Update 2 on 30 July 2024

1. Login into SAP

At your SAP, click on “Meine Reisen und Spesen”.

2. Click on your desired Trip

In my case, I will show example in Austria.

 

Click “Welter” to proceed.

3. At the Main Page with 4 steps

Step 1: Verify every information especially Kontierung (Your project number)

Next, click on checkbox with * and then proceed with “Belege erfassen”

Step 2: Add all related claims

Step 3: Validate

There are two options: Save it for future or Sent it to financial department

4. Final step

  • Prepare all the original receipts and keep a copy with you.
  • Print out the above from system
  • Put the documents at “Dienstreisen Folder” at Regina’s place
  • Bring the folder to Uni Post Office at 1st floor of old building.

Booking a Trip for a Conference/Visit/Summer School

Iye Szin Ang wiki_phds

Put your documents at TravelApplications/drafts

Here: https://cloud.cps.unileoben.ac.at/index.php/f/977844

Create the folder with your name

 

Travel Planning Checklist

Approval and Registration

  • Initial Planning: Check for a reasonable flight itinerary. Check if 1-2 days before and after the event have a substantially lower price. 
  • Obtain Approval: Secure trip approval from Elmar. Argue according to the initial planning.
  • Travel System Entry: Request Regina to input the trip details into the travel system. Specify which days are for official duties (e.g., conference, lab visits) and which are for personal stay. Provide Regina with the proof of acceptance, or reason to travel.

Booking Essentials

  • Accommodation and Commute Options: Provide a comparison spreadsheet of different options within the budget. Opt for reasonable over the cheapest options.
  • Booking Approval: Get approved by Elmar.
  • Accommodations and Commute: After obtaining approval, book your stay, conference registration, accommodations, etc. 

Travel Insurance

  • Carry Insurance Documentation: If traveling abroad, particularly outside the EU, bring a printed copy of the university’s or other relevant insurance policy

Visa Requirements

  • Include Embassy Commute: If a visa is necessary, incorporate the embassy commute in the travel system and communicate this to the secretary for travel cost reimbursement.
  • Visa Application Time: Visa application efforts are recognized as working hours.

After the Travel

  • Receipts: After the end of the trip, provide Regina with all the receipts, invoices, and tickets from:
      • Airplanes, trains, buses, and boats: tickets, invoices, bank statement
      • Accommodation: invoice, bank statement
  • Registrations: invoice, bank statement
  • etc.

 

Important Notes

  • OEBB Trains: The chair has a membership with OBB, please book the ticket in the user’s name. You can obtain the user’s login information from Regina.
  • After the travel: Keep all original receipts and submit them to Regina after returning.
  • Report Everything: Due to Austrian law for work insurance coverage, you must inform Regina by email if you will be outside the university zone during working hours, even for a few hours.
  • Private Stay: A private stay cannot exceed 50% of the duration of the working days. For example, if a conference is for six days, your private stay must be a maximum of three days. Otherwise, the university will cover only 50% of the flight tickets and hotel.

Tips:

  • Credit card with travel coverage (check if hospitalization is included for overseas)

Organizing Wiki Page Categories

Iye Szin Ang wiki_phds

Here’s a guide on how to label your categories effectively:

  1. wiki_phds: This category should encompass all aspects of your day-to-day life as a PhD student.

  2. wiki_road_to_thesis: Include guidelines, tips, and resources related to various stages of thesis writing, from proposal development to final defense preparations.

  3. wiki_hard_software: Use this category to share information, tutorials, and updates about the hardware and software used in your research projects.

  4. wiki_scientific_research_aspects: Discuss methodologies, data analysis techniques, experimental setups, and anything else related to the scientific rigor of your work.

  5. wiki_teaching_aspects: This category is dedicated to sharing insights, strategies, and resources for effective teaching, whether it’s leading a seminar, designing a course, or mentoring undergraduates.

  6. wiki_career_aspects: This category covers everything related to career development and professional growth.

The category label determines where the post will appear in its respective section.