Faculty of Science

Login for PhD students/staff at UCPH Login for others

International School of Chemometrics - PROGRAMMING
Provider: Faculty of Science

Activity no.: 5328-27-00-00	There are 36 available seats
	Enrollment deadline: 18/01/2027

Place	Department of Food Science
Date and time	19.04.2027, at: 00:00 - 23.04.2027, at: 16:00
Regular seats	40
Lecturers	Rasmus Bro
ECTS credits	2.50

Contact person	Beatriz Quintanilla Casas E-mail address: beatriz@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 for PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved for PhD students at other faculties and universities. Seats 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. Anyone can apply for the course, but if you are not a PhD student, 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 International School of Chemometrics (ISC) 2027 is a three-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 THREE different and independent modules. The students should enroll to each module they would like to follow. WEEK 01 - PROGRAMMING MODULE (2.5 ECTS) Introduction to Programming (MATLAB, R and Python) for Multivariate Data Analysis Since the very beginning, the ISC has always counted with a first week of Introduction to MATLAB for Multivariate Data Analysis. It was a week that worked very well, but we wanted to create something special. We have had many requests regarding Python and R. Therefore, we have decided to create a new module that includes the three major languages! How does it work? - Due to time constraints, the lessons will be based on pre-recorded videos, all structured on a learning platform, together with specific activities, exercises and quizzes where the students can learn one, two or all three languages at their own pace. - The topics are: 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. We are planning between 4 - 6 hours of recordings per language. - The three courses are simultaneous. One per language: 1) The students have access to ALL THE MATERIAL AND VIDEOS for the three courses. It is up to the student which language to follow. 2) ALL THE EXACT SAME MATERIAL (examples, exercises, etc.) is reproduced in the three languages. The videos and material will be released at the beginning of the ISC-2027, and will remain available until the end of 2027. Only the students who signed up officially will have access to the videos and material. It is really ADVISABLE that the students run the material and practice during the first week. In that week, we will create a consultancy platform where the teachers will be available to answer your questions. Previous knowledge needed: None Software needed: MATLAB, R, Python WEEK 02 - BASICS MODULE (2.5 ECTS) A basic introduction to data types, data pre-processing and the most used unsupervised and supervised methods for exploration, regression and classification. - PCA & SIMCA: Principal Component Analysis is 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. PCA-based classification models (namely, SIMCA) will also be explored. - PLS & PLSDA: 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 for both regression and classification. Moreover, the preprocessing of different types of data will also be addressed in the seminar as a prerequisite for using the data optimally. - Validation, Advanced Preprocessing & Variable Selection: This seminar provides insight into model validation and advanced preprocessing methods, as a follow-up on regression. It also focuses on state-of-the-art variable selection methods, covering both classical, iterative, model-based and nature-inspired algorithms. It 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. - Non-Linear Classification: As the main linear classification methods are explored early in the week, this seminar focuses on non-linear Classification models, such as Support Vector Machines and Random Forests. The seminar consists of theoretical sessions, accompanied by a series of exercises designed to understand the fundamentals of the methods. - DoE-ASCA: The seminar introduces the Design of Experiments (DOE). The course will highlight the critical points to address when designing experiments. Some classical designs will be discussed (Full Factorial, Plackett-Burman, Central Composite) together with more advanced approaches like D-Optimal Designs. Besides, a special session will be given as an introduction 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. The teachers that work with particular software will notify it to the students in advance and you can only expect practical support in the software language of the teacher. WEEK 03 - CHALLENGES MODULE (2.5 ECTS) The last week of the school brings advanced topics in chemometrics, including decomposition methods for matrices and multiway data, imaging and deep learning models. - MCR: The module will address the theoretical description and hands-on application of Multivariate Curve Resolution (MCR). MCR is a multivariate resolution (unmixing) method that can provide the description of a multicomponent data set through a bilinear model of chemically meaningful profiles, e.g., when analyzing an HPLC-DAD data set, MCR can provide the real elution profiles and the related UV spectra for each compound in the sample. It has applications in diverse fields, such as process analysis, chromatographic data, hyperspectral images or environmental data, in any context where a mixture analysis problem can be encountered. The module focuses mainly on the algorithm MCR-ALS (Multivariate Curve Resolution-Alternating Least Squares), and hands-on work will be done using a dedicated free GUI interface adapted to MATLAB environment. Applications will cover many of the areas mentioned above. - HSI: Hyperspectral imaging is an important analytical tool in a growing number of areas, including the chemical sciences, process monitoring and forensics, cultural heritage, and remote and standoff sensing. Images are acquired using a wide variety of spectroscopic techniques, including, but not limited to, infrared, mass spectroscopy, and Raman, among others, and it can be confusing how to efficiently extract information from the sizable, multi-wavelength images. This course will cover a variety of multivariate methodologies that can be applied to the analysis and interpretation of hyperspectral data, starting with principal components analysis (PCA) and using linked plots. Other techniques that will be discussed will include maximum autocorrelation factors, maximum difference factors, multivariate curve resolution (end-member extraction), target detection and targeted anomaly detection. This course will utilize HYPER-Tools. The course content will be useful for those involved in chemical, food, pharmaceutical, and medical imaging, as well as remote and standoff imaging. - MULTIWAY: Multi-way data is gaining popularity due to the capability of scientific devices to generate data with at least three dimensions (elution time – mz channel – samples, excitation-emission – sample, etc). Therefore, learning the basics of multi-way analysis will help to extract the most from that complex data structure. Methods such as parallel factor analysis (PARAFAC) and PARAFAC2 will be studied and applied to various examples. For the latter, the software PARADISe (PARAFAC2-based Deconvolution and Identification System) will be used. - Deep Learning: This seminar aims at providing a basic introduction to the techniques which may be used in all those situations when a linear relation is not enough to provide accurate results (e.g. due to the presence of multiple sources of variability). The most important aspects of data modelling will be considered (classification and calibration). Topics such as different artificial neural network architectures (shallow learning and deep learning) will be covered. - GLUE: This seminar is the final seminar of the school. We will take a close look at all the common mistakes that even experienced people will make when doing multivariate analysis. We will cover exploration, calibration, interpretation, visualisation and many other subjects. And always with a focus on the most common problem, as well as a sounder alternative. Additionally, a comprehensive overview of all the models studied at the school will be provided. Previous knowledge needed: Basic Chemometrics. Software needed: Feel free to work with MATLAB, Python or R, or any other software that you consider (e.g. Unscrambler). The teachers that work with particular software will notify it to the students in advance. Learning outcomes 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 AI 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 two weeks of the School are dedicated to physical on-site training. Type of Assessment The course is completed by attending all sessions. Participation and engagement during practical exercises will be evaluated. 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 professor (beatriz@food.ku.dk) Guest Lecturers - Prof. José Amigo Rubio, University of the Basque Country. Co-coordinator. - 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 Carlos de Cos, The MathWorks. - Assoc. Prof. Sergey Kucheryavskiy, University of Aalborg. - Dr. Anders Krogh Mortensen, The AI Lab. Dates 19-23/04/2027: Week 01 - Programming 26-30/04/2027: Week 02 - Basic 03-07/05/2027: Week 03 - Challenges Expected frequency Once per year Course location Frederiksberg Campus Deadline for registration 18/01/2027 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. 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: DKK 3.000 • PhD student from foreign university: DKK 3.000 • Master's student from Danish university: 0 DKK • Master's student from foreign university: DKK 3.000 • Non-PhD student employed at a university (e.g., postdocs): DKK 3.000 • Non-PhD student not employed at a university (e.g., from a private company): DKK 8.400 Participants will receive an email with a link and instructions for payment after completing the course. 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