Skip to main content
MyCourses MyCourses
  • Schools
    School of Arts, Design, and Architecture (ARTS) School of Business (BIZ) School of Chemical Engineering (CHEM) –sGuides for students (CHEM) – Instructions for report writing (CHEM) School of Electrical Engineering (ELEC) School of Engineering (ENG) School of Science (SCI) Language Centre Open University Library Aalto university pedagogical training program UNI (exams) Sandbox
  • Service Links
    MyCourses - MyCourses instructions for Teachers - MyCourses instructions for Students - Teacher book your online session with a specialist - Digital tools for teaching - Personal data protection instructions for teachers - Workspace for thesis supervision Sisu Student guide Courses.aalto.fi Library Services - Resourcesguides - Imagoa / Open science and images IT Services Campus maps - Search spaces and see opening hours Restaurants in Otaniemi ASU Aalto Student Union Aalto Marketplace
  • ALLWELL?
    Study Skills Guidance and support for students Starting Point of Wellbeing About AllWell? study well-being questionnaire
  •   ‎(en)‎
      ‎(en)‎   ‎(fi)‎   ‎(sv)‎
  • Toggle Search menu
  • Hi guest! (Log in)

close

Can not find the course?
try also:

  • Sisu
  • Courses.aalto.fi

PHYS-E0549 - Introduction to Machine Learning for Materials Science D, Lecture, 5.9.2022-11.10.2022

This course space end date is set to 11.10.2022 Search Courses: PHYS-E0549

  1. Home
  2. Courses
  3. School of Science
  4. department of...
  5. phys-e0549 - ...
 
Syllabus
 

General

  • General

    General

    Description - Introduction to machine learning in materials science

    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.

    "Introduction to 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. The course introduces different machine learning methods and provides examples for their application in materials science. The tutorials provide hands-on experience for the different methods. In the subsequent Project in Machine Learning for Materials Science course you will be able to apply the newly learned knowledge to your own data.

    Course level

    The course is intended for students who have completed their Bachelor's degree and have a basic understanding of machine learning or material science and a keen interest interdisciplinary science. Some programming experience or Python knowledge is required to take the course.

    Credits

    3 ECT are awarded for the course. 

    Assessment

    The course grade is pass/fail. The passing criteria is to attend at least 5 of the 6 tutorial sessions.

    Course structure and workload 

    The course is taught in Period 1

    • 6 x 2 h lectures on machine learning in materials science
    • 6 x 2h hands-on tutorial sessions

    There is no homework for the course and no final exam.

    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 hands-on experience with Python notebooks.
    • have used different machine learning methods in Python.
    • 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.

    Teachers
    • Prof. Patrick Rinke
    • Dr. Armi Tiihonen
    • Dr. Matthias Stosiek

    Course dates

    5.9-11.10.2022


    • icon for activity
      ForumAnnouncements Forum

Course home

Course home

Next section

Pre-assessment►
Skip Upcoming events
Upcoming events
Loading There are no upcoming events
Go to calendar...
  • PHYS-E0549 - Introduction to Machine Learning for Materials Science D, Lecture, 5.9.2022-11.10.2022
  • Sections
  • General
  • Pre-assessment
  • Home
  • Calendar
  • Learner Metrics

Aalto logo

Tuki / Support
Opiskelijoille / Students
  • MyCourses instructions for students
  • email: mycourses(at)aalto.fi
Opettajille / Teachers
  • MyCourses help
  • MyTeaching Support form
Palvelusta
  • MyCourses rekisteriseloste
  • Tietosuojailmoitus
  • Palvelukuvaus
  • Saavutettavuusseloste
About service
  • MyCourses protection of privacy
  • Privacy notice
  • Service description
  • Accessibility summary
Service
  • MyCourses registerbeskrivining
  • Dataskyddsmeddelande
  • Beskrivining av tjänsten
  • Sammanfattning av tillgängligheten

Hi guest! (Log in)
  • Schools
    • School of Arts, Design, and Architecture (ARTS)
    • School of Business (BIZ)
    • School of Chemical Engineering (CHEM)
    • –sGuides for students (CHEM)
    • – Instructions for report writing (CHEM)
    • School of Electrical Engineering (ELEC)
    • School of Engineering (ENG)
    • School of Science (SCI)
    • Language Centre
    • Open University
    • Library
    • Aalto university pedagogical training program
    • UNI (exams)
    • Sandbox
  • Service Links
    • MyCourses
    • - MyCourses instructions for Teachers
    • - MyCourses instructions for Students
    • - Teacher book your online session with a specialist
    • - Digital tools for teaching
    • - Personal data protection instructions for teachers
    • - Workspace for thesis supervision
    • Sisu
    • Student guide
    • Courses.aalto.fi
    • Library Services
    • - Resourcesguides
    • - Imagoa / Open science and images
    • IT Services
    • Campus maps
    • - Search spaces and see opening hours
    • Restaurants in Otaniemi
    • ASU Aalto Student Union
    • Aalto Marketplace
  • ALLWELL?
    • Study Skills
    • Guidance and support for students
    • Starting Point of Wellbeing
    • About AllWell? study well-being questionnaire
  •   ‎(en)‎
    •   ‎(en)‎
    •   ‎(fi)‎
    •   ‎(sv)‎