Credits: 3

Schedule: 10.09.2019 - 03.12.2019

Contact information for the course (applies in this implementation): 

Course time and place

10.9-3.12.2019, Tuesdays 14-16h, U401.

Contact information
Dr. Milica Todorovic, Prof. Patrick Rinke

Learning outcomes

After completion of the course you

  • learned the importance of machine learning in materials science.
  • have gained an overview of different machine learning methods.
  • have gained insight into materials representations for efficient machine learning.
  • are familiar with materials datasets and data analytics.
  • can approach a range of different problems with suitable machine learning methods.
  • can follow a presentation (e.g. conference or seminar) on machine learning in materials science.
  • can plan, execute, document and present a small research project.
  • can give peer feedback.

Teaching Period (valid 01.08.2018-31.07.2020): 

varies

Content (valid 01.08.2018-31.07.2020): 

The course is held by the staff or by visiting scientists. The topics of the course change every year. Includes for example:

Quantum Technologies (3 cr), IV (Spring 2019, 2020), Gheorghe-Sorin Paraoanu 

 

Details on the course content (applies in this implementation): 

Machine learning (ML) techniques enable us to infer relationships from a large amount of seemingly uncorrelated input data. Their predictive power has made them central to product development in IT and we already use them in daily life (Amazon, Netflix, etc.). Physical sciences have been slow to capitalize on the promise of ML, even though their computational implementation is suited to modern simulation techniques. Materials science has recently benefited from a number of ML applications to materials discovery and design (featuring neural networks, genetic algorithms, regression methods, compressed sensing and Bayesian optimisation), that promise to accelerate development of novel technologies. Machine learning for materials science is an exciting new discipline that is now being taught at Aalto University.

"Machine learning in materials science" is a project-led lecture course for graduate students who wish to acquire key skills in this cross-disciplinary research field. Introductory lectures on materials science and machine learning will be followed by tutorial exercises, student-led seminars and a light research project. The research work will be carried out in mixed teams (free choice of topic) and provides ideal opportunities for learning on realistic materials science datasets (experimental or computational). The target of the project work is to get hands-on experience in this field and explore the performance of different ML methods on different datasets, which already constitutes an important contribution to the field and the career of course participants.


Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation): 

The course grade is pass/fail. It is possible to take the course with two levels of difficulty for 3/5 ECT:

  • level 1: attending classes and tutorials (at least 5 out of 6 contact sessions), surveying the field and giving a seminar (pass with 3 ECT).
  • level 2: level 1, with seminar work replaced by participating in the project work. (pass with 5 ECT).

Details on calculating the workload (applies in this implementation): 

Period 1 and 2 (10.9.-3.12.)

  • 4 x 2 h introductory lectures on machine learning in materials science
  • 2 x 4h hands-on tutorial sessions
  • 3 x 2h student seminar presentations
  • project work in small teams
  • 3 x 2h project checkpoint contact sessions
There is no homework for the course and no final exam.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

Tfy-3.4510 Special Course in Physics

Course Homepage (valid 01.08.2018-31.07.2020): 

https://mycourses.aalto.fi/course/search.php?search=PHYS-E0541

Grading Scale (valid 01.08.2018-31.07.2020): 

0-5, may be graded with pass/fail

Details on the schedule (applies in this implementation): 

See MyCourses page.

Description

Registration and further information