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.

WEEK 03 - INTERMEDIATE MODULE (2.5 ECTS)
Intermediate topics on Chemometrics. Variable Selection, Classification and Design of Experiments – ASCA.

This seminar contains several general topics:
- VARSEL - Variable selection methods: In this one-day hands-on course on variable selection, you will become familiar with state-of-the-art variable selection. This will cover both classical, iterative, model-based and nature-inspired algorithms.

I will also provide you with some pros and cons of the different methods, as well as suggestions on how to operate them even more effectively.

The cases you will be working with can be done in R, Matlab (w/ wo PLS-toolbox) or Python, all as you see fit.

However, I only provide the necessary toolboxes for MATLAB.
- CLASS – Classification Methods: The course will deal with the main linear classification methods like Discriminant Analysis, Partial Least Squares Discriminant Analysis (PLS-DA) and SIMCA.

We will see both theoretical aspects and practical applications. It will also deal with Support Vectors Machine and Random Forests.
The seminar consists of theoretical sessions, accompanied by a series of exercises designed to understand the fundamentals of the three aforementioned methods.

- DoE - Design of Experiments and ANOVA-Simultaneous Components Analysis ASCA:
The course gives an introduction to the Design of Experiments. The course will highlight the critical
points to address when designing our experiments. Some classical designs will be discussed (Full
Factorial, Plackett-Burman, Central Composite) together with more advanced approaches like DOptimal
Designs. Besides, a special session will be given as an introductory to ANOVA – Simultaneous Components Analysis (ASCA).

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.
- R Studio
- Matlab
- The Classification toolbox can be freely downloaded from here:
https://michem.unimib.it/download/matlab-toolboxes/classification-toolbox-for-matlab/

Intended learning outcome for the students who complete the 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 with basic topics and go all the way to 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
Main coordinator: Rasmus Bro, Professor (rb@food.ku.dk)
Co-coordinator: Beatriz Quintanilla Casas, Assistant profesor (beatriz@food.ku.dk)


Guest Lecturers
- Prof. José Amigo Rubio, University of the Basque Country.
- Prof. Morten A. Rasmussen, University of Copenhagen.
- Assoc. Prof. Asmund Rinnan, University of Copenhagen.
- Assoc. Prof. Agnieszka Smolinska, Maastricht University.
- Prof. Davide Ballabio, University of Milano-Bicocca.
- Prof. Anna de Juan, University of Barcelona.
- Dr. Neal Galhaguer, Eigenvector Research.
- Dr. Carlos de Cos, The Mathworks.
- Assoc. Prof. Sergey Kucheryavskiy, University of Aalbrog.
- Dr. Anders Krogh Mortensen, The AI Lab.
- Prof. Federico Marini, University of Rome La Sapienza.


Dates
05-09/04/2027: Week 01 - Programming
12-16/04/2027: Week 02 - Basic
19-23/04/2027: Week 03 - Intermediate
26-30/04/2027: Week 04 - Challenges


Expected frequency
Once per year


Course location
Frederiksberg Campus


Registration
Deadline for registration
18/01/2027


Requirements for signing up
No extra requirement


Seats to PhD students from other Danish universities will be allocated on a first-come, first served basis and according to the applicable rules.
Applications from other participants will be considered after the deadline for registration.