The Graphics and Vision research group of the University of Basel has produced several online courses, which explain the theoretical concepts behind Scalismo and their practical application using Scalismo.
The first course is offered once a year as a MOOC on FutureLearn. Its focus lies on the concept of Gaussian processes, and how these can be used to model shape variability. It also discusses simple algorithms for fitting models to surfaces and images. The course videos and articles from previous course runs can freely be accessed even outside of an official course run. The relevant links are collected on this site:
The second course focuses on probabilistic shape model fitting using a Bayesian approach. It discusses how shape model fitting can be formulated as a problem in Bayesian inference and how the computations can be performed using Markov Chain Monte Carlo methods. The course is designed as a follow up to the first course. All the practical examples and tutorials and implemented using Scalismo.
The last course covers similar ground, but is targeted at researchers in computer vision. It explains the principles of Bayesian inference and model fitting in the context of the problem of analyzing 2D photographs of human faces using a statistical face model.