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180146 SE Introduction to Cognitive Science II: Key Topics in Cognitive Science (2018S)
Prüfungsimmanente Lehrveranstaltung
Labels
1.Termin (Vorbesprechung): Mo 5. März 2018, 9:00 - 11:00
HS 2i d. Inst. f. Philosophie, NIG, 2. StockWeitere Termine werden bei der Vorbesprechung bekannt gegeben!
HS 2i d. Inst. f. Philosophie, NIG, 2. StockWeitere Termine werden bei der Vorbesprechung bekannt gegeben!
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Do 15.02.2018 00:00 bis Do 08.03.2018 23:59
- Abmeldung bis Sa 31.03.2018 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Mittwoch 14.03. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
- Mittwoch 11.04. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
- Freitag 13.04. 09:45 - 13:00 Hörsaal 2i NIG 2.Stock C0228
- Mittwoch 18.04. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
- Freitag 20.04. 09:45 - 13:00 Hörsaal 2i NIG 2.Stock C0228
- Mittwoch 25.04. 09:45 - 13:00 Hörsaal 3B NIG 3.Stock
- Freitag 27.04. 09:45 - 13:00 Hörsaal 2i NIG 2.Stock C0228
- Freitag 04.05. 09:45 - 13:00 Hörsaal 2i NIG 2.Stock C0228
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
presentation, team/project work, participation in discussions, participation in the tutorial
Mindestanforderungen und Beurteilungsmaßstab
" Advanced knowledge and understanding of central questions, key concepts, and approaches in cognitive science in their historical context
" Knowledge and understanding of key notions of philosophy of science and their implications for cognitive science
" Ability to reflect upon, compare, and relate different disciplinary approaches in terms of their respective aims, key-concepts, and methods
" Knowledge and understanding of different models of interdisciplinarity
" Ability to read, present, and discuss primary scientific literature
" Ability to organise work in physical and virtual environments
" Ability to sharpen/focus/channel analytical and critical thinking
" Ability to solve problems in an interdisciplinary team
" Ability to organise project work in an interdisciplinary team
" Ability to relate findings and methods of cognitive science to ethical questions
" Ability to reflect upon personal competences and develop individual motivation and interests
" Knowledge and understanding of key notions of philosophy of science and their implications for cognitive science
" Ability to reflect upon, compare, and relate different disciplinary approaches in terms of their respective aims, key-concepts, and methods
" Knowledge and understanding of different models of interdisciplinarity
" Ability to read, present, and discuss primary scientific literature
" Ability to organise work in physical and virtual environments
" Ability to sharpen/focus/channel analytical and critical thinking
" Ability to solve problems in an interdisciplinary team
" Ability to organise project work in an interdisciplinary team
" Ability to relate findings and methods of cognitive science to ethical questions
" Ability to reflect upon personal competences and develop individual motivation and interests
Prüfungsstoff
Introductory articles
P. Thagard, Cognitive Science. In: The Stanford Encyclopedia of Philosophy, Fall 2014 Edition, Edward N. Zalta (ed.)
https://plato.stanford.edu/archives/fall2014/entries/cognitive-science/
Gideon Lewis-Kraus (2016) The great A.I. awakening. In: The New York Times Magazine, December 14, 2016, New York.
E.A. Lee, Plato and the Nerd. MIT Press, 2017.
Detailed reading list (target articles)
Deep learning
Yann LeCun, Yoshua Bengio, Geoffrey Hinton (2015) Deep learning. Nature Vol. 521, pp. 436-444. https://www.researchgate.net/publication/277411157_Deep_Learning
Theory-free science
Chris Anderson, The end of theory. Wired Magazine. https://www.wired.com/2008/06/pb-theory/
Understanding computer-generated models
J. Yosinski et al., Understanding neural networks through deep visualization, Deep Learning Workshop; 31st Int. Conf. Machine Learning, 2015. http://yosinski.com/media/papers/Yosinski__2015__ICML_DL__Understanding_Neural_Networks_Through_Deep_Visualization__.pdf
Robotics and autonomy
D.Vernon et al., Embodied cognition and circular causality: on the role of constitutive autonomy in the reciprocal coupling of perception and action. Frontiers in Psychology, 2015. https://www.frontiersin.org/articles/10.3389/fpsyg.2015.01660/full
Technoscience
Kastenhofer & Schmidt; Technoscientia est Potentia?: Contemplative, interventionist, constructionist and creationist idea(l)s in (techno)science, Poiesis & Praxis, Vol. 8 (2), 2011. https://link.springer.com/article/10.1007/s10202-011-0101-2
Creativity, art, and poiesis
Mark Coeckelbergh, The art, poetics, and grammar of technological innovation as practice, process, and performance. AI & Society, 2017. https://link.springer.com/article/10.1007/s00146-017-0714-7
P. Thagard, Cognitive Science. In: The Stanford Encyclopedia of Philosophy, Fall 2014 Edition, Edward N. Zalta (ed.)
https://plato.stanford.edu/archives/fall2014/entries/cognitive-science/
Gideon Lewis-Kraus (2016) The great A.I. awakening. In: The New York Times Magazine, December 14, 2016, New York.
E.A. Lee, Plato and the Nerd. MIT Press, 2017.
Detailed reading list (target articles)
Deep learning
Yann LeCun, Yoshua Bengio, Geoffrey Hinton (2015) Deep learning. Nature Vol. 521, pp. 436-444. https://www.researchgate.net/publication/277411157_Deep_Learning
Theory-free science
Chris Anderson, The end of theory. Wired Magazine. https://www.wired.com/2008/06/pb-theory/
Understanding computer-generated models
J. Yosinski et al., Understanding neural networks through deep visualization, Deep Learning Workshop; 31st Int. Conf. Machine Learning, 2015. http://yosinski.com/media/papers/Yosinski__2015__ICML_DL__Understanding_Neural_Networks_Through_Deep_Visualization__.pdf
Robotics and autonomy
D.Vernon et al., Embodied cognition and circular causality: on the role of constitutive autonomy in the reciprocal coupling of perception and action. Frontiers in Psychology, 2015. https://www.frontiersin.org/articles/10.3389/fpsyg.2015.01660/full
Technoscience
Kastenhofer & Schmidt; Technoscientia est Potentia?: Contemplative, interventionist, constructionist and creationist idea(l)s in (techno)science, Poiesis & Praxis, Vol. 8 (2), 2011. https://link.springer.com/article/10.1007/s10202-011-0101-2
Creativity, art, and poiesis
Mark Coeckelbergh, The art, poetics, and grammar of technological innovation as practice, process, and performance. AI & Society, 2017. https://link.springer.com/article/10.1007/s00146-017-0714-7
Literatur
Extended reading list
Deep learning
Yann LeCun, Yoshua Bengio, Geoffrey Hinton (2015) Deep learning. Nature Vol. 521, pp. 436-444.
Olivier Temam, Enabling future progress in machine-learning. VLSI Circuits, 2016.
Collobert et al., Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12, 2011.
Theory-free science
Chris Anderson, The end of theory. Wired Magazine.
Fulvio Mazzocchi, Could big data be the end of theory in science? Science & Society, 2015.
Byung-chul Han; Agonie des Eros. Matthes & Seitz, 2015.
Byung-chul Han; Die Errettung des Schönen. Fischer, 2015.
Understanding computer-generated models
J. Yosinski et al., Understanding neural networks through deep visualization, Deep Learning Workshop; 31st Int. Conf. Machine Learning, 2015
J. Burrell, How the machine 'thinks': Understanding opacity in machine learning algorithms. Big Data & Society, 2016,
Robotics and autonomy
D.Vernon et al., Embodied cognition and circular causality: on the role of constitutive autonomy in the reciprocal coupling of perception and action. Frontiers in Psychology, 2015.
T. Ziemke, On the role of emotion in biological and robotic autonomy; Biosystems, Vol. 91 (2), 2008.
Technoscience
Kastenhofer & Schmidt; Technoscientia est Potentia?: Contemplative, interventionist, constructionist and creationist idea(l)s in (techno)science, Poiesis & Praxis, Vol. 8 (2), 2011
G. Hottois, Technoscience et sagesse? In: Gramm et al. Ding und System, diaphanes, 2015.
Creativity, art, and poiesis
Mark Coeckelbergh, The art, poetics, and grammar of technological innovation as practice, process, and performance. AI & Society, 2017.
A. Roepstorff, J. Niewöhner, S. Beck; Enculturing brains through patterned practices, Neural Networks 23, 2010.
L.R.Varshney et al., A big data approach to computational creativity. arXiv:1311.1213v1 [cs.CY] 5 Nov 2013
Deep learning
Yann LeCun, Yoshua Bengio, Geoffrey Hinton (2015) Deep learning. Nature Vol. 521, pp. 436-444.
Olivier Temam, Enabling future progress in machine-learning. VLSI Circuits, 2016.
Collobert et al., Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12, 2011.
Theory-free science
Chris Anderson, The end of theory. Wired Magazine.
Fulvio Mazzocchi, Could big data be the end of theory in science? Science & Society, 2015.
Byung-chul Han; Agonie des Eros. Matthes & Seitz, 2015.
Byung-chul Han; Die Errettung des Schönen. Fischer, 2015.
Understanding computer-generated models
J. Yosinski et al., Understanding neural networks through deep visualization, Deep Learning Workshop; 31st Int. Conf. Machine Learning, 2015
J. Burrell, How the machine 'thinks': Understanding opacity in machine learning algorithms. Big Data & Society, 2016,
Robotics and autonomy
D.Vernon et al., Embodied cognition and circular causality: on the role of constitutive autonomy in the reciprocal coupling of perception and action. Frontiers in Psychology, 2015.
T. Ziemke, On the role of emotion in biological and robotic autonomy; Biosystems, Vol. 91 (2), 2008.
Technoscience
Kastenhofer & Schmidt; Technoscientia est Potentia?: Contemplative, interventionist, constructionist and creationist idea(l)s in (techno)science, Poiesis & Praxis, Vol. 8 (2), 2011
G. Hottois, Technoscience et sagesse? In: Gramm et al. Ding und System, diaphanes, 2015.
Creativity, art, and poiesis
Mark Coeckelbergh, The art, poetics, and grammar of technological innovation as practice, process, and performance. AI & Society, 2017.
A. Roepstorff, J. Niewöhner, S. Beck; Enculturing brains through patterned practices, Neural Networks 23, 2010.
L.R.Varshney et al., A big data approach to computational creativity. arXiv:1311.1213v1 [cs.CY] 5 Nov 2013
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Sa 10.09.2022 00:19
Als Ergebnis des Seminars sollen Studierende in der Lage sein, relevante Literatur on- und offline zu suchen, ausgewählte Literatur zu einzelnen wichtigen philosophischen Grundlagen der Kognitionswissenschaften zu studieren, zu vergleichen und sie in historischen Kontext zu setzen, zusammenzufassen, zu präsentieren und zu diskutieren.