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Lecturer(s)
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Matlach Vladimír, Mgr. Ph.D.
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Zámečník Hadwiger Lukáš, Mgr. Ph.D.
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Course content
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Theory and Paradigms - Programming methods and paradigms, procedural, functional, object-oriented approach - Software design, diagramming, agile development - Debugging, refactoring, documentation Introduction to Python - Variables, lists, dictionaries and other data structures - Loops, functions, recursion - Working with files, text, tables and multimedia - Didactic exercises to understand algorithmization Practical problems - Text data processing and modelling, creating retrieval systems, etc. - Using existing text and image processing libraries
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Learning activities and teaching methods
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Lecture, Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming)
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Learning outcomes
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An introduction to the Python programming language for solving complex text, image, or machine learning tasks. Students will gain practical programming skills through a series of didactically designed examples aimed towards applications in the Digital Humanities. The course leverages skills from previous courses, in particular Data Analysis 1, Introduction to Quantitative Methods, and Fundamentals of DH. In particular, the course aims to provide sufficient background for subsequent machine learning courses using Python.
Knowledge of problems of philosophy of science and the related terminology
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Prerequisites
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For doctoral students only.
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Assessment methods and criteria
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Seminar Work
(1) Completion of own and previously consulted project.
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Recommended literature
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Curd, M. - Cover, J. A. (1998). Philosophy of Science: The Central Issues. New York.
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Fajkus, B. (2005). Filosofie a metodologie vědy - vývoj, současnost a perspektivy.. Praha: Academia.
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Giere, R. N. (2009). Science Without Laws. Chicago.
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Ochrana, F. (2009). Metodologie vědy. Praha.
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Rosenberg, A. Philosophy of Science. .
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Rosenberg, A. (2008). Philosophy of Social Science. New York and London.
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Van Fraassen, B. C. (2002). The Empirical Stance. Boston.
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