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CHEM-LV - Machine Learning and Applications in Chemistry, 22.03.2017-28.04.2017

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Syllabus

General

  • General

    General

    Content

    Machine learning (ML) is rising in importance as a new approach in addition to the more traditional statistical methods in data analysis and predictive modelling in many fields ranging from natural language processing to materials science. In chemistry, one of the recent efforts has been to predict complex physical quantities based on molecular and materials geometry, without the need of computationally demanding molecular modelling or ab initio quantum chemistry / DFT calculations. Fast screening of a large amount of structures is needed for developing chemical compounds and materials solutions for novel and desired functionalities. The purpose of the course is to give a first introduction to the vast field of machine learning (basic concepts, methods and workflow) and survey how ML can be used in chemistry. The general aim is to convey the exciting potential of ML, which you may later on meet or employ in your own work. 

    The course involves working on exercise problems/assignments, giving a presentation and writing a home essay (the exact requirements will be fixed in the beginning of the course). There is no final exam.

    Course level and prerequisites
    This is an introductory course. Basics of numerical data handling and ability to follow code examples (in python) is expected. 

    Learning outcomes

    After this course you

    • understand the basics of a ML approach and its concepts from problem setting to evaluating the goodness of a model
    • can apply ML to solve a simple task  
    • have an overview how ML is used in contemporary chemistry
    • can analyze and evaluate a chemistry-related ML article
    Assessment methods

    The exact requirements for passing will be fixed in the beginning of the course.

    Lectures and computer exercises (22.3. - 28.4.)

    Lectures are planned on Wednesdays 12-14 and Thursdays 10-12. Exercises are planned on Fridays 13-15.

    First lecture: Wednesday 22.3. in B202b 12:15 - 14:00

    For students: Remember to register also in Oodi.

    Teacher
    D.Sc.(Tech) Mikko Hakala

       

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  • 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
  • CORONAVIRUS INFO
    • Koronavirus - tietoa opiskelijalle
    • Coronavirus - information for students
    • Coronavirus - information för studerande
    • Koronaviruksen vaikutus opiskeluun: kysymyksiä ja vastauksia
    • Effects of the coronavirus on studies: questions and answers
    • Coronaviruset och studierna: frågor och svar
    • Corona help for teachers
  • Service Links
    • MyCourses
    • - Instructions for Teachers
    • - Teacher book your online session with a specialist
    • - Digital tools for teaching
    • - Personal data protection instructions for teachers
    • - Instructions for Students
    • - Workspace for thesis supervision
    • WebOodi
    • Into portal for students
    • 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
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    • Starting Point of Wellbeing
    • About AllWell? study well-being questionnaire
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