About
Advanced Computing is a discipline that approaches complex computational problems from both theoretical and practical angles. It views algorithms, i.e., the human or mechanical processes of transforming data, as subjects for systematic study. The goal is to understand their limitations and capabilities and to use these insights to provide novel and more efficient algorithmic solutions.
Target
The specialization is addressed to students with a background in computer science, computer engineering or mathematics who can prove that they have a solid grounding in elementary algorithms, programming skills and basic knowledge of discrete mathematics. Students from other scientific and technological backgrounds are also welcome but they may be asked to take a bachelor-level course that guarantees that the requirements are covered.
Specialization coordinator
| Person | Department | |
|---|---|---|
| Duch Brown, Amalia | CS | duch@cs.upc.edu |
Specialization teachers
Technical Competences of each Specialization
ADVANCED COMPUTING
- CEE3.1
Capability to identify computational barriers and to analyze the complexity of computational problems in different areas of science and technology as well as to represent high complexity problems in mathematical structures which can be treated effectively with algorithmic schemes. - CEE3.2
Capability to use a wide and varied spectrum of algorithmic resources to solve high difficulty algorithmic problems. - CEE3.3
Capability to understand the computational requirements of problems from non-informatics disciplines and to make significant contributions in multidisciplinary teams that use computing.
Subjects
Specialization compulsory subjects
- Combinatorial Problem Solving (CPS-MIRI)
- Randomized Algorithms (RA-MIRI)
- Advanced Data Structures (ADS-MIRI)
- Computational Complexity (CC-MIRI)
Specialization complementary subjects
- Complex and Social Networks (CSN-MIRI)
- Algorithmic Game Theory (AGT-MIRI)
- Algorithmics for Data Mining (ADM-MIRI)
- Algorithms for VLSI (AVLSI-MIRI)
Elective
Mandatory
Master's Thesis
Mandatory Common course
Semester 1
Algorithmic Methods for Mathematical Models
(AMMM - 6 ECTS)
(AMMM - 6 ECTS)
Concurrence, Parallelism and Distributed Systems
(CPDS - 6 ECTS)
(CPDS - 6 ECTS)
Randomized Algorithms
(RA - 6 ECTS)
(RA - 6 ECTS)
Statistical Modelling and Design of Experiments
(SMDE - 6 ECTS)
(SMDE - 6 ECTS)
Semester 2
Algorithmics for Data Mining
(ADM - 6 ECTS)
(ADM - 6 ECTS)
Advanced Data Structures
(ADS - 6 ECTS)
(ADS - 6 ECTS)
Computational Complexity
(CC - 6 ECTS)
(CC - 6 ECTS)
Combinatorial Problem Solving
(CPS - 6 ECTS)
(CPS - 6 ECTS)
Semester 3
Algorithmic Game Theory
(AGT - 6 ECTS)
(AGT - 6 ECTS)
Algorithms for VLSI
(AVLSI - 6 ECTS)
(AVLSI - 6 ECTS)
Complex and Social Networks
(CSN - 6 ECTS)
(CSN - 6 ECTS)
Semester 4
Master's Thesis
(TFM - 30 ECTS)
(TFM - 30 ECTS)
Seminar activities
The activities to obtain the SIRI credits can be done in any semester of the master's degree. Consult the detail of the seminars.
Seminars of Innovation and Research in Informatics
(SIRI - 6 ECTS)
(SIRI - 6 ECTS)
Elective
Interdisciplinary Innovation Project
(I2P - 6 ECTS)
(I2P - 6 ECTS)
Data Visualization
(VD - 6 ECTS)
(VD - 6 ECTS)
Machine Learning Systems in Production
(MLOps - 6 ECTS)
(MLOps - 6 ECTS)
Computer Vision
(CV - 6 ECTS)
(CV - 6 ECTS)
Introduction to Quantitative Linguistics
(IQL - 6 ECTS)
(IQL - 6 ECTS)
Introduction to Research
(I2RM3 - 3 ECTS)
(I2RM3 - 3 ECTS)
Introduction to Research
(I2RM6 - 6 ECTS)
(I2RM6 - 6 ECTS)