Master’s Programme Committee for Engineering Physics 2/22
22.3.2022
The second Master’s Programme committee meeting for Engineering Physics had quite many topics on the agenda, but the meeting ended up being very short as most topics were accepted unanimously without further discussion.
One of the main tasks of the committee is accepting Master’s thesis topics and evaluations(gradings) of completed theses, which will be done at every meeting if needed. This time all proposed topics and grades were accepted without opposing voices. The previous meeting sparked some discussion on the timeline of completion of the thesis and its effect on grading, which you read from the previous summary by Into
https://fiirumi.mylly.fyysikkokilta.dev/t/teknillisen-fysiikan-maisteriohjelman-koulutusneuvoston-kokous-1-22/453
Curricula 2022- 2024
The main topic for the meeting was approving the curricula for Engineering Physics majors, available minors and changes in courses for the years 2022-2024. There were no major changes in the structures and most practical changes consist of new, modified or deleted courses.
New and deleted courses
Most of the new courses of the applied physics department (PHYS-XXXX) and their learning outcomes were already presented in the summary of the previous committee meeting (in English). However, one of these courses, Machine Learning for Materials Science, was decided to be split into 2 different courses, an introduction course and a project course, both still being 5 ECTS.
PHYS-E0549 Introduction to Machine Learning for Materials Science
Learning outcomes (EN): After completion of the course you will be able to: 2 • Have knowledge of different ML methods • Have practical experience of applying ML methods to materials science examples • Identify research questions in material science (MS) that can be solved by machine learning (ML) • Consider which ML methods might be best for tackling different MS problems
PHYS-E0550 Project in Machine Learning for Materials Science
Learning outcomes (EN): After completion of the course you will: • Have deepened your understanding of machine learning (ML) in materials science (MS) • Be able to carry out a project in ML for MS • Understand different types of MS datasets for ML • Perform basic data analysis of datasets • Assess and improve the performance of ML models • Select a suitable MS data representation as input for ML • Critically comment on ML applications in MS (on quality of data analysis, suitability of chosen ML method, quality of assessment of ML performance, etc).
Another mostly technical a change is the deletion of the special courses, which from now on will be moved under the same course name “PHYS_EV****Variable content course”.
PHYS-E0541 - Special Course in Physics V D
PHYS-E0542 - Special Course in Theoretical Physics V D
PHYS-E0545 - Special Course in Computational Physics V D
PHYS-E0582 - Special Course in Advanced Energy Technologies 2 V D
PHYS-E058201 - Special Course in Advanced Energy Technologies 2 V D: Radiation damage in metals and semiconductors
Changes in majors:
Materials Physics and Quantum Technology
- Added to the optional math courses MS-E2121 Linear Optimization
- Added to long major:
- PHYS-E6575 Experimental Methods in Physics
- PHYS-E6574 Radiation damage in materials
- PHYSE0549 Introduction to Machine Learning for Materials Science
- PHYS-E0550 Project in Machine Learning for Materials Science
- CHEM-E4210 Molecular Thermodynamics D
- CHEM-E5150 Surfaces and Films
- Study cooperation agreement allows students in this major to take quantum technology courses from University of Helsinki, which can be used to replace some of the optional courses. More info and the current list of these courses can be already found here:
https://into.aalto.fi/display/enengphys/Studies+in+quantum+technology
Advanced Energy Technologies
- Removed from optional courses CHEM-E5100 - Solid State Materials and Phenomena
- Added to long major PHYS-E6574 Radiation damage in materials
Changes in minors organized by the department:
Engineering Physics
- Removed PHYS-C0230 Klassinen dynamiikka
- Added PHYS-E0549 Introduction to Machine Learning for Materials Science
- Added PHYS-E0550 Project in Machine Learning for Materials Science
Aalto Nuclear Safety
- Removed ENY-C2001 Termodynamiikka ja lämmönsiirto
- Added EKO-C2001 Termodynamiikan ja lämmönsiirron perusteet
- Added PHYS-E6574 Radiation damage in material
On top of these changes there were some technicalities such as changes in course codes and some modified or added texts for the masters program’s Aalto Into pages made for clarification.
Announcements
Towards the end of the meeting some statistics and announcements were presented which included:
- Autumn 2021 course feedback on courses organized by the department of applied physics. Overall average assessment 4.07 on the scale of 1-5 without major outliers.This included both masters and bachelors level courses.
- SCI Exchange applications 31.1.2022. The number of students applying for an exchange this year was relatively high with a total of 169 applications out of which 158 were accepted. In previous years the number of applications and acceptances has been (130/118) in 2021 and (111/108) in 2020.
- SCI Master’s thesis awards 2021 were presented. In total 4 students/graduates were awarded, none of whom were in Engineering Physics majors.