Faculty of Science

International School of Chemometrics - BASICS
Provider: Faculty of Science

Activity no.: 5328-26-00-01	There are 38 available seats
	Enrollment deadline: 01/03/2026

Place	Department of Food Science
Date and time	20.04.2026, at: 09:00 - 24.04.2026, at: 17:00
Regular seats	40
Lecturers	Rasmus Bro
ECTS credits	2.50

Contact person	Rasmus Bro E-mail address: rb@food.ku.dk

Enrolment Handling/Course Organiser	PhD Administration SCIENCE E-mail address: phdcourses@science.ku.dk

Enrolment guidelines This is a specialised course where 50% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved to other applicants. Seats will be allocated on a first-come, first-served basis and according to the applicable rules. Anyone can apply for the course, but if you are not a PhD student at a Danish university (except CBS), you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to applicants on the waiting list. Aim and Content The ISC-2026 is a four-week school designed to introduce different key aspects of Data Science and Machine Learning in different branches of science (chemistry, food & feed, physics, environmental, political economics, etc). It is addressed to BSc, MSc, PhD students/post-docs, professors, as well as industrial and private researchers. IMPORTANT: The ISC-2026 is structured in FOUR different and independent modules: PROGAMMING, BASICS, INTERMEDIATE, CHALLENGES. The students CAN CHOOSE WHICH ONES TO DO. Please make sure to register individually for each course you intend to participate in. BASICS MODULE: A basic introduction to Chemometrics, data types, data pre-processing, PCA, Multivariate Linear Regression, and Linear Algebra This seminar contains several general topics: - PCA – PREPRO – REGRESS: Data exploration and regression. Principal Component Analysis has become the most powerful and versatile tool for exploring data tables in Analytical Sciences. Here, we present a course to illustrate the main benefits and drawbacks of PCA when applied to various types of analytical data, including spectroscopy, environmental assessment, sensory experiments, performance experiments, and chromatography. Moreover, the preprocessing of different types of data will also be addressed in the seminar as a prerequisite for exploring the data optimally. If PCA is the keystone of pattern recognition methods, PLS is the keystone of multivariate calibration methods. This seminar will give a general overview of different multivariate calibration strategies (Multilinear Regression, Principal Component Regression), and will focus on Partial Least Squares regression. - LinAl: Linear Algebra. The Foundation for chemometric modelling is Linear Algebra. Why do the algorithms work? Why are the models meaningful? A math-derived answer to these questions can be found using linear algebra. The seminar will focus on hands-on experience with some fundamental linear algebra concepts, including rank, determinant, inverse, pseudo inverse, eigenvalues, singular value decomposition, orthogonality and basis sets. We will analyze a few real-life datasets, but the purpose of the seminar is to be a proficient mechanic unravelling the black box of algorithms and models, while other courses will teach you how to drive a car. Important: Refer to the detailed calendar for additional information. The modules are not divisible. Therefore, the entire week counts as a single course. Previous knowledge needed: None Software needed: Feel free to work with Matlab, Python or R, or any other software that you consider (e.g. Unscrambler). The teachers will work with: - Matlab - PLS_Toolbox / SOLO. IMPORTANT: For the PLS_Toolbox / SOLO, a fully functional demo will be available for the School. Teachers: José Manuel Amigo, Beatriz Quintanilla, Morten A. Rasmussen. See guest lecturers for further information. Learning outcomes Intended learning outcome for the students who complete the ISC-2026 complete course: Knowledge • Learn the basics of data analysis methods. • Learn to handle data and create proper datasets and libraries for further analysis • Learn critical thinking regarding Machine Learning, Chemometrics and IA Skills • Develop their own data analysis protocols • Code basic algorithms and the resources available for data analysis • Apply the acquired knowledge to any problem related to their own research Competences • Understand the structure of a vast number of data types and the issues derived from the data • Independent thinking for the solution of their problems • Interaction with other peers and teachers Target Group The course is specifically addressed to PhD students. Additionally, the course attracts a high number of BSc, MSc, postdoctoral researchers, and professors. Another relevant audience is Industry. The course receives students from 2 to 3 companies every year. Recommended Academic Qualifications None specifically required. We start from basic topics and go all the way to a more advanced topics. Research Area Chemometrics, machine learning, spectroscopy, artificial intelligence, programming, statistics Teaching and Learning Methods The seminars of the International School of Chemometrics will comprise a mix of presentations from world-leading researchers, combined with practical and theoretical exercises in data analytics software, which will provide students with hands-on experience in applying the tools taught. The exercises are done under the supervision of the teachers. The initial week of programming offers instruction in three different languages (MATLAB, R and Python), and all the instruction in this part is based on e-learning. The remaining three weeks of the school are dedicated to physical on-site training. Type of Assessment The course is completed by attending and development during the practical exercises will be evaluated by interest of the student. Literature Peer-reviewed papers provided during the course. Course coordinator Rasmus Bro, Professor, rb@food.ku.dk Guest Lecturers - Prof. Rasmus Bro, University of Copenhagen. Main coordinator. Teacher in CHALLENGES (Multiway and GLUE). - Prof. José Amigo Rubio, University of the Basque Country. Primary person responsible for day-to-day business operations throughout the entire School. Teacher in PROGRAMMING (MATLAB), BASICS and CHALLENGES (GLUE). - Assistant Prof. Beatriz Quintanilla, University of Copenhagen. Primary person responsible for day-to-day business operations throughout the entire School. Teacher in BASICS and CHALLENGES (Multiway and GLUE). - Prof. Morten A. Rasmussen, University of Copenhagen. Teacher in BASICS (LinAl). - Assoc. Prof. Asmund Rinnan, University of Copenhagen. Teacher in INTERMEDIATE (VarSel). - Assoc. Prof. Agnieszka Smolinska, Maastricht University. Teacher in INTERMEDIATE (DoE-ASCA). - Prof. Davide Ballabio, University of Milano-Bicocca. Teacher in INTERMEDIATE (CLASS). - Prof. Anna de Juan, University of Barcelona. Teacher in CHALLENGES (MCR). - Dr. Neal Galhaguer, Eigenvector Research. Teacher in CHALLENGES (HYPER). - Dr. Carlos de Cos, The Mathworks. Teacher in CHALLENGES (NonLin). - Assoc. Prof. Sergey Kucheryavskiy, University of Aalbrog. Teacher in PROGRAMMING (R). - Dr. Anders Krogh Mortensen, The AI Lab. Teacher in PROGRAMMING (Python). - Prof. Federico Marini, University of Rome La Sapienza. Teacher in CHALLENGES (GLUE). Dates PROGRAMMING: 13th April – 17th April, 2026. BASICS: 20th April – 24th April, 2026 INTERMEDIATE: 25th April – 1st May, 2026 CHALLENGES: 4th May – 8th May, 2026 Detailed calendar ISC-2026 Week 01 - Online PROGRAMMING 13-april 14-april 15-April 16-april 17-april Programming Programming Programming Programming Programming Week 02 - BASIC 20-april 21-april 22-april 23-april 24-april PCA LinAl PREPO REG REG Week 03 - INTERMEDIATE 25-april 26-april 27-april 28-april 01-may VARSEL VARSEL CLASS CLASS DoE - ASCA Week 04 - CHALLENGES 04-may 05-may 06-may 07-may 08-may MCR MCR NonLin NonLin GLUE - 1000M HYPER HYPER MULTIWAY MULTIWAY Course location Frederiksberg Campus Course fee • PhD student enrolled at SCIENCE: 0 DKK • PhD student from Danish PhD school Open market: 0 DKK • PhD student from Danish PhD school not Open market: 3000 DKK • PhD student from foreign university: 3000 DKK • Master's student from Danish university: 0 DKK • Master's student from foreign university: 3000 DKK • Non-PhD student employed at a university (e.g., postdocs): 3000 DKK • Non-PhD student not employed at a university (e.g., from a private company): 8400 DKK Cancellation policy • Cancellations made up to two weeks before the course starts are free of charge. • Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000 • Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000 • No-show will result in a fee of DKK 5.000 • Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 Course fee and participant fee PhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types. In addition to the course fee, there might also be a participant fee. If the course has a participant fee, this will apply to all participants regardless of participant type - and in addition to the course fee.

Click the search button to search Courses.

See which courses you can attend and when

Jan	Feb	Mar	Apr
May	Jun	Jul	Aug
Sep	Oct	Nov	Dec

Publication of new courses
All planned PhD courses at the PhD School are visible in the course catalogue. Courses are published regularly.