The curriculum in High Performance Computing and Data Engineering trains graduates skilled in modern techniques of high performance computing technologies and methodologies and big data management.
This curriculum comes with specific focusses, taking the form of predefined study plans, listed below:
In case you wish to give the curriculum your own twist, you need to follow the general study plan of the curriculum, described below.
| Courses | ECTS | |
|---|---|---|
| I year (60 ECTS) | ||
| I semester | ||
| Probability and Statistics for Scientific Computing | 6 | |
| High Performance Computing (+) | 6 | |
| Cloud Computing (+) | 6 | |
| One course from Core Group A (+) | 6 | |
| One course from Core Group B | 6 | |
| One course from Core Group C | 6 | |
| #colspan# | ||
| II semester | ||
| Deep Learning | 6 | |
| One course from Core Group D | 6 | |
| One course from Core Group E | 6 | |
| One course from Core Group F | 6 | |
| #colspan# | ||
| II year (60 ECTS) | ||
| High Performance Computing and Data Infrastructures | 6 | |
| One course from Complementary Group | 6 | |
| Elective courses | 12 | |
| Internship | 12 | |
| Thesis | 24 | |
(+): Integrated courses (modules combined in a single course)
| Core Group A Courses | ECTS |
|---|---|
| Advanced programming (*) | 6 |
| Software Development Methods | 6 |
| Core Group B Courses | ECTS |
|---|---|
| Numerical Analysis (*) | 6 |
| Mathematical Optimization | 6 |
| Probabilistic Machine Learning | 6 |
| Core Group C Courses | ECTS |
|---|---|
| Introduction to Machine Learning (*) | 6 |
| Unsupervised Learning | 6 |
| Core Group D Courses | ECTS |
|---|---|
| Mathematical Optimization | 6 |
| Advanced High Performance Computing | 6 |
| Core Group E Courses | ECTS |
|---|---|
| Algorithms for Scientific Computing (*) | 6 |
| Advanced Algorithms for Scientific Computing | 6 |
| Core Group F Courses | ECTS |
|---|---|
| Data Management (*) | 6 |
| Advanced Data Management | 6 |
(*) These courses contain introductory material and they cannot be inserted in the study plan if a course with a corresponding content has been attended during the bachelor or in other educational programs. Please ask the program coordinator if you are unsure.
You can add complementary courses from the following groups
| Complementary Group Courses | ECTS |
|---|---|
| Advanced Data Management | 6 |
| Advanced Database Systems | 6 |
| Machine Learning Operations | 6 |
| Information Retrieval and Data Visualisation | 6 |
| Computer Vision and Pattern Recognition | 6 |
You have to add in the study plan elective courses from the following group:
| Elective Courses | ECTS | |
|---|---|---|
| All the courses in the previous tables | ||
| GPU and Parallel Programming | 6 | |
| Natural Language Processing | 6 | |
| Stochastic Modelling and Simulation | 6 | |
| Advanced Deep Learning and Kernel Methods | 6 | |
| Artificial Intelligence for Cyber-Physical Systems | 6 | |
| Bayesian Statistics | 6 | |
| Explainable and Reliable Artificial Intelligence | 6 | |
| Software Development Methods | 6 | |
| Other courses (****) | ||
(****) Other courses can belong to any field of studies and any program of the university, provided they are coherent with the training path of the student.