Agenda

Sunday

15:00 – 18:00 Registration & Laptop preparation

18:00 – 20:00 Welcome Reception

20:00 Dinner

Monday - Introductory material

Morning Session

8:15 – 08:45 Registration

08:45 – 09:00 Welcome

09:00 – 10:00 Matteo Pardo, Introduction to Pattern Recognition and Regression, Main Concepts and Underlying Hypothesis  Download

10:00 – 10:20 Coffee break

10:20 – 11:00 Matteo Pardo, Introduction to Pattern Recognition and Regression, Main Concepts and Underlying Hypothesis (continued)  Download

11:00 – 12:20 Basic Statistics, Basic Algebra Santiago Marco  Download

12:30 Lunch

Afternoon Session

17:30 – 17:45 Coffee break

17:45 – 20:00 Santiago Marco, Basic Statistics, Basic Algebra (continued) & LAB: Introduction to MATLAB: Basic Statistics and Algebra

20:30 Dinner

Tuesday – Preprocessing, exploratory data analysis, linear methods

Morning Session

08:30 – 10:15 Santiago Marco, Signal and Data Preprocessing: Digital Filtering and Spectral Analysis, Basic Feature Extraction  Download

10:15 – 10:30 Coffee break

10:30 – 11:45 Jan Mitrovics, Linear Methods in Smart Sensor Arrays: PCA, LDA, MLR, PCR, PLS  Download
11:45 – 12:20 Jan Mitrovics, Luuk Mettes, Activities in the Data Format Standardization group  Download and  Download

12:30 Lunch

Afternoon Session

17:30 – 17:45 Coffee break

17:45 – 19:45 Jan Mitrovics, LAB: Hands on Linear Methods

19:45 – 20:15 Waltraud Kessler, 3 way PLS regression with The Unscrambler

20:30 Dinner

Wednesday - Novel tools in Chemometrics

Morning session

08:30 – 10:15 Rasmus Bro, Design of Experiments and Multiway Analysis I  Download and  Download

10:15 – 10:30 Coffee break

10:30 – 12:15 Romà Tauler, Multiway Analysis II  Download

12:30 Lunch

Afternoon Session

17:30 – 17:45 Coffee break

17:45 – 19:45 Romà Tauler, Rasmus Bro, LAB: Chemometric Methods

20:30 Dinner

Thursday - Statistical pattern recognition I

Morning Session

08:30 – 10:00 Ricardo Gutierrez-Osuna, Statistical classifiers: Bayesian decision theory and density estimation  Download

10:00 – 10:20 Coffee break

10:20 – 11:20 Ricardo Gutierrez-Osuna, Statistical classifiers: Bayesian decision theory and density estimation  Download

11:20 – 12:20 Krishna Persaud, Artificial Neural Networks: Multilayer Perceptrons and Radial Basis Functions  Download

12:30 Lunch

Afternoon Session

17:30 – 17:45 Coffee break

17:45 – 19:45 Ricardo Gutierrez-Osuna, Matteo Pardo, LAB: Classification

20:30 Social Dinner

Friday- Statistical pattern recognition II

Morning Session

09:00 – 10:00 Julian W. Gardner, Feature Selection Techniques  Download

10:00 – 10:20 Coffee break

10:20 – 12:00 Matteo Pardo, Algorithm Independent Learning  Download

12:00 Concluding Remarks & Farwell

Lectures

In the following the content of the lectures is outlined in keywords (always backed upped with examples, tips, and applications):

• Introductory examples, Bayesian Decision Theory, taxonomy, parametric/non-parametric approaches,
• Basis statistics, probabilities, inference, distributions, decision theory, tests
• Algebra, vector, matrix, products, eigensystems, SVD,
• Signal processing, filtering, spectral analysis, feature extraction, sampling, errors, noise
• PCA, LDA, MLR, PCR, PLS,  concept, scores, loadings, comparison, examples
• Design of experiments, objectives, basics,
• Multiway analysis, three way data, PARAFAC, N-PLS, PARAFAC2, factor analysis, TUCKER, MCR, hard vs. soft-modelling,
• Pattern classification, classifiers, probability theory, Bayesian decision theory, kernel density estimation, nearest neighbours, linear discriminant functions,
• Artificial Neural Networks (ANN), Neurons, Multilayer Perceptrons (MLP), back propagation, radial basis functions (RBF)
• Feature selection, search algorithms
• Error, complexity control

Computer lab

A new session was introduced in this short course. For the first time a computer lab was established. The goal of those practical exercises in the afternoons was to integrate the participants and to have a two way learning process. The participants had the opportunity to practice the theory learnt in the morning and to ask the present experts questions.

The Lecturers

• Jan Mitrovics, JLM Innovation, Germany
• Prof. Dr. Rasmus Bro, Dept. of Dairy and Food Science, The Royal Veterinary and Agricultural University, Denmark
• Prof. Dr. Romà Tauler, Dept. Environmental Chemistry, CID-CSIC, Spain
• Dr. Ricardo Gutierrez-Osuna, Dept. Computer Science, Texas A&M University, USA
• Prof. Dr. Krishna Persaud, Dept. Instrumentation and Analytical Science, UMIST, UK
• Prof. Dr. Julian W. Gardner, Dept. Electrical and Electronic Engineering, University of Warwick, UK
• Dr. Santiago Marco, Dept. Electronics, Universitat de Barcelona, Spain
• Dr. Matteo Pardo, National Institute for Matter Physics & University of Brescia, Italy
• Prof. Waltraud Kessler, Reutlingen University, Germany