Course - Experimental Design, Modelling and Machine Learning - KJ6020
KJ6020 - Experimental Design, Modelling and Machine Learning
New from the academic year 2021/2022
Examination arrangement: Project
|Evaluation||Weighting||Duration||Grade deviation||Examination aids|
This course is an introduction to experimental design and machine learning methods for modeling and data analysis with emphasis on applications to chemistry, biotechnology, process chemistry, material science, and physics. The goal of the course is to provide knowledge on methods that can be used to extract useful information from complex data sets or physical processes. The course gives knowledge about the planning of efficient experiments (how can we set up and execute experiments so that we can get as much information with as few experiments as possible), modeling (how to build and validate robust models), and methods that can be used to extract and discover useful information in complex data sets. Specifically, the following themes will be covered: design and planning of experiments, analysis of variance, regression, modeling with validation, techniques from machine learning for solving problems such as classification, clustering, analysis of multivariate data sets, and the use of neural networks for modeling.
The course gives knowledge about:
- Planning of experiments with analysis and interpretation of results from experimental designs.
- Important steps for building and validating models with an emphasis on prediction.
- Use of unsupervised analysis methods such as principal component analysis, explorative cluster methods for the analysis and interpretation of large data sets.
- Robust regression methods (e.g. partial least squares) for analysis, interpretation, and prediction for large data sets.
- Use of elementary machine learning techniques for classification.
- Use of neural networks for modeling.
Learning methods and activities
The course will be centered around 4 seminars (physical or digital). Each seminar consists of two days and includes lectures, group-based exercises, and discussions
Further on evaluation
The examination in the course is in the form of a project that the participants execute using one or more of the approaches from the course. The topic of the project will be decided in collaboration with the participants and tailored to their interests. The project can be executed in collaboration or individually. Each participant will have to deliver an individual project report describing their work and findings.
Admission to a programme of study is required:
Recommended previous knowledge
Basic mathematics (including some linear algebra), statistics, and chemistry.
The course material will be defined at the start of the course.
Credits: 7.5 SP
Study level: Further education, higher degree level
Term no.: 1
Teaching semester: SPRING 2022
Language of instruction: English
Department with academic responsibility
Department of Chemistry
Department with administrative responsibility
Centre for Continuing Education and Professional Development
Examination arrangement: Project
- Term Status code Evaluation Weighting Examination aids Date Time Digital exam Room *
- Spring ORD Project 100/100 INSPERA
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"