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-2027 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-2027 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.

WEEK 02 - BASICS MODULE (2.5 ECTS)
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.

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.

- The topics will be:
1) Language structure; 2) vectors and matrices; 3) arrays; 4) Basic operations; 5) Graphical Output, beginning; 6) Data structures and datasets; 7) Loops; 8) Conditions; 9) Another tricks in control flow; 10) Functions; 11) Data preprocessing; 12) Principal Component Analysis; 13) Graphical Output, advanced; 14) Images.

 

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.