3rd Cycle Studies
Fluency in English, both oral and written.
The QM module is taught mainly as lectures. Some particular case-studies are presented and discussed. In the SC module, formal presentations are kept to a minimum, and instead students are involved in task-based group activities.
To conduct empirical research through quantitative methods and to develop scientific communication skills, both oral and written. Acquiring competencies in analysis and synthesis, organization and planning, oral and written communication, knowledge of a foreign language, communication with non-experts, working in an international context, decision making, research, critical reasoning, professional ambition, self-criticism and self-assessment.
1. Quantitative methods
1.1 Research methods in computer science: The scientific method; case study methodology.
1.2 Sampling techniques
1.3 Exploratory data analysis.
1.4 Statistical inference
1.5 System modelling, simulation and analysis
2. Scientific communication
2.1 Effective reading/textual organization/summarizing: introduction to extensive reading techniques; textual analysis; summary of an original article;
2.2 The grammar of scientific discourse: complex noun phrases; impersonal structures;
2.3 Abstracts: analysis of different kinds of abstract; cohesion and coherence;
2.4 Oral presentations: planning a short oral presentation, focus on delivery.
Luís Filipe dos Santos Coelho Paquete
Workshop about the state-of-the-art of their research field.: 30.0%
Student performance and a written assignment.: 35.0%
Critically reviews an article that contains experimental analysis.: 35.0%
- Bailey, S. Academic Writing: A Handbook for International Students. London & New York: Routledge. 2006.
- Breach, M. Dissertation Writing for Engineers and Scientists. New Jersey: Prentice Hall. 2008.
- Lester, J.D & Lester, J. Writing Research Papers: A Complete Guide. New York & London: Longman. 2004.
- Penrose, A.M & Katz, S.B. Writing in the Sciences: Exploring Conventions of Scientific Discourse. London & New York: Longman, 2009.
- Hinkelmann, K. and Kempthorne, O., Design and Analysis of Experiments. I and II (2nd ed.). Wiley, 2008.
- Sheldon, R., Probability and Statistics for Engineers and Scientists, Academic Press, 2009
- Cohen, P., Empirical Methods for Artificial Intelligence, MIT Press, 1995
- Bartz-Beielstein, T., Chiarandini, M., Paquete, L., Preuss, M, Experimental Methods for the Analysis of Optimization Algorithms, Springer, 2010;
- McGeoch, C., A Guide to Experimental Algorithmics, Cambridge University Press, 2012.