FAIR approach to scientific data management. Software stack for data intensive application on HPC resources. Programming Tools and methods for data intensive application.

Kernel Methods, .Biologically inspired neural networks, Geometric deep learning, Robust deep learning, inductiuve bias.

Single-core optimization: vectorization, effective exploiting of pipelines and instruction-level parallelism, accessing and using hardware performance counters. Usage of advanced features and techniques of both MPI and OpenMP will be covered. Among other topics in MPI: the effective usage of MPI on large-scale systems with non-blocking communications and complex topologies, the remote inter-node memory access and the intra-node shared memory, massively parallel I/O. Among other topics in OpenMP: advanced affinity control, task decomposition, vectorizations and elements of heteogenous acceleration.

Stochastic processes. Brownian Motion. Martingales. Markov Processes. The Stochastic Integral. Stochastic Calculus. Stochastic Differential Equations.

Advanced programming in C++ and Python. Data Types, error handling. Object-oriented programming. Best practices. Unit testing. How to combine C++ and Python.

The course extends basic statistical models through the introduction of multilevel/ hierarchical models, semi and non-parametric regression models, spline functions, penalized likelihood approaches (such as LASSO and Ridge regression models), hierarchical models for estimating smooth regression functions, analysis of functional data. Applications to complex datasets and simulation approaches will be illustrated during the course using the Stan ecosystem and the statistical software R.

Advanced topics in bayesian machine learning, variational inference, theory of neural networks, unsupervised learning

Pattern matching problem. Algorithms for text mining. Algorithms for clustering. Algorithms for graph mining. Data stream models.

Definition of algorithm, and asymptotic analysis. Basic data structure. Sorting algorithms. Matrix multiplication. Basic graphs algorithms, transitive closure, connectedness, shortest path algorithms, routing problems. Pattern matching problem. Basic string algorithms.

Logic-based methods for CPS: automata, temporal logic, and modelling formalisms. Monitoring and verification. Integration with learning: logic-based explainable predictors for time-series, anomaly detection, logic-driven reinforcement learning. Simulation-based testing, falsification, and system-design.