Teaching Module Hourly volume ECTS
Scientific Computing Project Management (SCPM) UE1
– Software engineering for scientific computing (24h)
– Development environments (8h)
– Oriented-object programming (24h)
56 H6
Methods for Scientific computing (SC) UE2
– Convex optimization (16h)
– Linear algebra for data science and scientific computing (24h)
– Algorithm complexity and scalability (16h)
56 H 6
High performance architecture and programming (HPC) UE3
– Technologies, hardware (20h)
– Parallel programming techniques on the CPU (18h)
– Parallel programming techniques on the GPU (18h)
56 H 6
Machine Learning algorithms (ML) UE4
– Statistics and probabilities (24h)
– Numerical algorithms for machine learning (32h)
56 H 6
High Performance Algorithms and Deep Learning (HPDA) UE5
–   Deep Learning algorithms and architectures (20h)
–   Data processing platforms (Hadoop, Spark) (20h)
–   Massive data visualization techniques and tools (16h)
56 H 6
Numerical modelling and simulation (NMS) UE6
– Discrete systems (12h)
– Continuous systems (12h)
– Optimization and inverse problems (12h)
– Stochastic and statistical methods (12h)
– Signal and image processing (12h)
– Scientific Machine Learning (12h)
– NLP with Deep Learning (12h)
– Randomized Linear Algebra (12h)
24 H 3
Digital Society (NS) UE7
– HPC society (6h)
– Digital and intellectual property (6h)
– Data ethics (6h)
– Virtualization and security (6h)
24 H 3
Projects and case study (CSP) UE8 56 H 6
Conferences 20 H3