Pontifícia Universidade Católica do Paraná (PUCPR), combines international campus with top ranked undergraduate and graduate programs, and innovative research projects in partnership with international organizations and universities. PPGia is one of its graduate programs.
The Graduate Program in Informatics (PPGIa) prepares researchers to excel in the fast-growing and dynamic field of informatics. Established in 1996, PPGIa offers research degrees at Masters and PhD levels. Every year, around 15 Masters and 15 PhD candidates enrolled.
Data Science: Machine Learning: Theory and Applications, Bioinformatics, Music Information Retrieval
Artificial Intelligence: Agent Systems, Computational Linguistics, Knowledge-Based Systems
Systems Engineering: Cyberphysical Systems: IoT, Software Engineering, Security and Privacy Computer Vision
Software Agents: Adaptive and Autonomous Agents, Learning Agents, Process Mining.
Security and Privacy: Access Control, Identity Management, Intrusion Detection System, Computer Forensics.
Music Information Retrieval: Automatic Labelling in Audio and Video, Music Information Retrieval.
Knowledge Discovery and Machine Learning: Machine Learning, NLP, Big Data Analytics, Information Retrieval.
Software Engineering: Software Quality, Software Metrics, Organizational Learning, Software Process Improvement.
Computer Networks and Communications: Network Management, IoT, Service Quality, Mobile Networks, Wireless Networks.
Computer Vision Information Retrieval, Pattern Recognition, Image Analysis, Biometry.
Opportunities for 2020
Content: Basic statistics: distributions, kurtosis and symmetry. Correlations: Pearson and spearman. Data Visualization. Exploratory Data Analysis: univariate and multivariate data analysis. Identification and treatment of missing values. Identification of outliers. Dimensionality reduction: PCA and t-SNE.
Content: Multilabel Classification. Hierarchical Classification. Data stream mining: definition, tasks, validation, assessment, and challenges. Concept drift and change detectors. Data Stream classification: instance-based, bayesian, decision trees, and ensemble-based approaches. Data stream regression: regression model trees, ensembles. Data stream clustering: main algorithms and challenges.