NOTE: All seminars include all the material that the student might need: 

- Slides of the course (pdf).

- Exercises

- Datasets

- Toolboxes

- Refreshments during the lessons (coffee, tea, candies, cookies, and other amenities). This, of course, if we are allowed due to the Covid-19 pandemic situation

- We do NOT provide: Matlab and lunch

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​HANDS-ON: 

Everyday, from 4 pm to 6 pm, every student will have the opportunity to ask whatever question remaining from any seminar and to work on their own data and check with us on the analysis workflow.

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SEMINAR 1 - PROGRAMMING

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. 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 online with pre-recorded videos.

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- 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. We are planning between 4 - 6 hours of recordings per language.

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- They are three simultaneous courses. One per language, BUT:

1) The student will 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) will be reproduced in the three languages.

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The videos and material will be released on April 28th, 2025, and will remain available until the end of 2025. 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 week from April 28th until May 2nd. During that period, we will create a consultancy platform where the teachers will be available to answer your questions and doubts.

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Dates and timetable: 28th of April - 2nd of May 

 

Previous knowledge needed: None

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Software needed: Matlab, R, Python

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Teachers: José Manuel Amigo (Matlab), Sergey Kucheryavskyi (R), Anders Krogh Mortensen (Phyton).

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ECTS: 1

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Prices: See the prices on the homepage of ISC.

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SEMINAR 2 - BASIC

A basic introduction to Chemometrics, data types, data pre-processing, PCA, Multivariate Linear Regression, and Linear Algebra

 

This seminar contains two general topics:

 

- EXPLORE - 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 show the main benefits and drawbacks of PCA when it is used for different kinds of analytical data: Spectroscopy, environmental assessment, sensory, experiments performance, chromatography, etc. Moreover, the preprocessing of different types of data will also be addressed in the seminar as a prerequisite for having the optimal possibility for exploring the data. 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.

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- 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. 

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Dates and timetable: 5th of May - 9th of May. From 9 am until 4 pm (CET) with a 1-hour lunch break (30 hours)

 

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.

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Teachers: José Manuel Amigo and Beatriz Quintanilla (EXPLORE), Morten A. Rasmussen (LINAL).

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ECTS: 2.5

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Prices: See the prices on the homepage of ISC.

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SEMINAR 3 - INTERMEDIATE

Intermediate topics on Chemometrics. Linear Classification, Variable selection methods, Multivariate Curve Resolution

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This seminar contains three general topics:

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- DoE - Design of Experiments: 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 D-Optimal Designs.

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- 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 give you some pros and cons of the different methods, and some suggestions for how to operate them even better. 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.

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- MCR - Multivariate Curve Resolution: 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 would 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. MCR can be applied to a single data matrix or to multiset structures formed by blocks of different information (data fusion). 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.

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Dates and timetable: 12th May - 16th of May. From 9 am until 4 pm (CET) with 1-hour lunch break (30 hours)

 

Previous knowledge needed: Basic multivariate data analysis and Matlab

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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.

- MCR-ALS toolbox: MCR-ALS toolbox can be freely downloaded here: https://mcrals.wordpress.com/download/mcr-als-2-0-toolbox/

- R Studio

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Teachers: Agnieszka Smolinska (DoE), Asmund Rinnan (VARSEL), Anna de Juan (MCR).

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ECTS: 2.5

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Prices: See the prices on the homepage of ISC.

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CHALLENGES

This week is composed of different and individual seminars for a further understanding of more and extremely useful analytical methods. Some of them are on the same dates. So please, verify the dates to avoid overlapping.

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SEMINAR 4 - OPT

Optimization methods in multivariate analysis

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This seminar will give an overview of different optimization methods that are extremely useful for optimizing hyperparameters in models. Methods like Particle swarm optimization (PSO) or Gauss-Newton will be taught and different examples discussed.

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Dates and timetable: 19th of May - 20th of May. From 9 am until 4 pm (CET) with 1-hour lunch break (30 hours)

 

Previous knowledge needed: Advanced multivariate data analysis.

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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

- Matlab

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Teacher: Federico Marini.

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ECTS: 1.5

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Prices: See the prices on the homepage of ISC.

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SEMINAR 5 - CLASS

Linear and nonlinear Classification​

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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 together with a collection of exercises designed to understand the fundamentals of the three abovementioned methods.

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Dates and timetable: 19th of May - 20th of May. From 9 am until 4 pm (CET) with 1-hour lunch break (12 hours)

 

Previous knowledge needed: Basic multivariate data analysis.

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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

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

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Teacher: Davide Ballabio

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ECTS: 1.5

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Prices: See the prices on the homepage of ISC.

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SEMINAR 6 - DL

Deep Learning.

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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). In this respect, the most important aspects of data modeling will be considered (classification and calibration). Topics such as different artificial neural network architectures (shallow learning and deep learning) will be covered. 

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Dates and timetable: 21st of May - 22nd of May. From 9 am until 4 pm (CET) with 1-hour lunch break (30 hours)

 

Previous knowledge needed: Basic multivariate data analysis

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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.

- Deep Learning Toolbox of Matlab

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Teacher: Carlos de Cos

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ECTS: 1.5

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Prices: See the prices on the homepage of ISC.

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SEMINAR 7 - METABO

Metabolomics data analysis

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Learning outcome: Be able to drive a project from a data set to both supervised and unsupervised analyses on human metabolome and health data

Tools: Advanced Python, Machine Learning techniques

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Day 1:
Morning - Python classes:
     Working with dataframes in Python and pandas
     Loading data from online and local sources, data processing
     Creating classes and functions
Afternoon - Metabolomic data processing
     Imputing metabolomic data, cleaning
     Working with Pipelines and machine learning libraries
     Classifier/Regressor pipelines

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Day 2:
Morning - Unsupervised learning in metabolomics
     Unsupervised learning - PCA
     Unsupervised learning - Clustering techniques
Afternoon - Supervised models in metabolomics
     Decision tree models
     Random Forests classifier and regressor
     Explainable machine learning to understand the results
 

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Dates and timetable: 21st of May - 22nd of May. From 9 am until 4 pm (CET) with 1-hour lunch break (12 hours)

 

Previous knowledge needed: Basic multivariate data analysis

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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:

- Advanced Python, Machine Learning techniques

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Teacher: Mario Lovric

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ECTS: 1.5

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Prices: See the prices on the homepage of ISC.

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SEMINAR 8 - GLUE (How not to make Chemometrics) and WORKSHOP

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This seminar is divided into two parts:

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- Morning: We will take a very close look at all the most common mistakes that even experienced people will do when doing multivariate analysis. We will cover exploration, calibration, interpretation, visualization and many other subjects. And always with a focus on what is the most common problem as well as a sounder alternative.

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- Afternoon: Any student of the School will be able to ask whatever question remaining from any seminar. Also, there will be time to work on your own data and check with us on the analysis workflow.

 

Dates and timetable: 7th of June. From 9 am until 4 pm (CET) with 1-hour lunch break (6 hours)

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Previous knowledge needed: Chemometrics

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Software: Up to you!

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Teacher: José Manuel Amigo, Beatriz Quintanilla and Rasmus Bro.

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ECTS: 0

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Price: 0 DKK (Approx. 0 euro / 0 USD)

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