Luca Calatroni - Teaching

Teaching @ UCA

Academic year 2021/2022 - 2022/2023

Inverse problems in image processing

MSc in Data Science and Artificial Intelligence

Lecturers: L. Blanc-Féraud, L. Calatroni (CNRS).

Teaching assistants: M. Lazzaretti, V. Stergipoulou.

Course website (Moodle): Click here (2021/2022) and here (2022/2023).

Teaching @ Unibo

Academic year 2022/2023

Smooth and non-smooth, convex and non-convex optimisation for imaging

PhD winter school on "Advanced methods for mathematical image analysis".

Course duration: 8 hours (lectures + exercise and lab classes)

Teaching assistants M. Lazzaretti (UniGe, UCA).

Course website and material: Click here.

Teaching @ École Peyresq 2020-2021

Short course: Bilevel optimisation models for hyperparameter estimation in imaging inverse problems

XV École GRETSI: Signal and image processing for the co-design of innovative imaging systems: physique, mathematics and algorithms

Organisers: L. Blanc-Féraud (I3S), A. Almansa (CNRS)

Course website: Click here

Material available upon request.

Teaching @ UCA

Academic year 2020/2021 - 2021/2022

Where art & mathematics meet

ECUE Histoire de l'histoire de l'art - HMEVHA1, Master civilisation culture et société

Lecturers: A. Zucker, R. M. Dessì, M. Lauwers (CEPAM)

Course website (MOODLE): Click here

LECT 1: Where art & mathematics meet + Mathematical challenges in cultural heritageLECT 3: Image osmosis for data fusion: some applications to cultural heritage

Material available upon request.

Teaching @ Università di Bologna

Academic year 2018/2019

Variational and PDE models for mathematical image processing

PhD Program in Mathematics.

Period: 9-17 May 2019.

Course website: click here.

LECT 1: Introduction: imaging inverse problems. Statistical, variational and PDE approaches.LECT 2: Review of functional and convex analysis. The space BV. The Rudin Osher Fatemi denoising model.LECT 3: Well-posedness of the ROF model and of deblurring/inpainting TV models. Optimality conditions.LECT 4: MATLAB implementation of TV denoising/inpainting models.LECT 5: Anisotropic diffusion & directional variational models. LECT 6: MATLAB implementation of automatic colour TV inpainting.LECT 7: Image osmosis.LECT 8: MATLAB implementation of face cloning via image osmosis.

Material available upon request.

Teaching @ École Polytechnique

Academic years 2017/2018, 2018/2019.

Discrete Mathematics (MAA 103): exercise classes

Bachelor Program, 1st semester.

Lecturer: I. Kortchemski.

TD 1: Sets. TD 2: Formal logic, truth tables, methods of proof.TD 3: Quantified statements.TD 4: Functions: injectivity, surjectivity, bijectivity.TD 5: Functions: images and preimages of sets.TD 6: Mathematical induction.TD 7: Review of the previous weeks.TD 8: Cardinality and combinatorics.TD 9: Binomial coefficients.TD10: Combinatorics - again.TD11: Permutations.TD12: The tower of Hanoi.TD13: Finite probability spaces.TD14: Independence and conditional probabilities.TD15: Random variables.

Mathematical Modelling (MAA 107): exercise classes

Bachelor Program, 2nd semester.

Lecturers: V. Bansaye, T. Mastrolia.

TD 1: Introduction to finance - Payment schedules.TD 2: The bead, the wire and the spring.TD 3: Preys VS. predators.TD 4: Rosenzweig MacArthur’s model.TD 5: Arbitrage-Binomial model and hedging.TD 6: Genetic drift, probability and speed of fixation.TD 7: Relaxation to equilibrium.

LECTURE NOTES

Teaching @ Università di Genova

Academic year 2015/2016.

Inverse problems and applications: MATLAB Lab

Università degli studi di Genova, Master Degree in Applied Mathematics, 2nd semester

Lecturers: C. Estatico, A. Sorrentino.

Course website: Click here

LAB 1: The image deblurring problem.LAB 2: Spectral filtering and parameter selection.LAB 3: Iterative regularisation.LAB 4: Bayesian approach: importance sampling.LAB 5: Bayesian approach: Metropolis-Hastings.

Material available upon request.

Mathematical models for applications and data analysis: theory and MATLAB Lab

Università degli studi di Genova, Master Degree in Applied Mathematics, 2nd semester

Organiser: Camelot Biomedical Systems.

LECT 1: Co-registration of biomedical images.LECT 2: Image segmentation. LECT 3 (by S. Cazzato): Machine learning and predictive analytics. LECT 4: Easy image denoising.

Material available upon request.

Teaching @ University of Cambridge

Academic years 2013/2014, 2014/2015.

- supervisor of the course Analysis II, Part IB of the Mathematical Tripos, Michaelmas 2013, Michaelmas 2014.

- supervisor of the course Topics in Analysis, Part II of the Mathematical Tripos, Michaelmas 2014.

Material available upon request.

Teaching @ Università di Pavia

Academic years 2009/2010, 2010/2011.

- teaching assistant of the course Calculus and elementary Probability of the Degree in Natural Sciences.

- teaching assistant of the course Geometry I of the Degree in Mathematics.

Material available upon request.