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180146 SE Introduction to Cognitive Science II: Key Topics in Cognitive Science (2018S)
Continuous assessment of course work
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!
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first serve).
- Registration is open from Th 15.02.2018 00:00 to Th 08.03.2018 23:59
- Deregistration possible until Sa 31.03.2018 23:59
Details
max. 25 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Wednesday
14.03.
09:45 - 13:00
Hörsaal 3B NIG 3.Stock
Wednesday
11.04.
09:45 - 13:00
Hörsaal 3B NIG 3.Stock
Friday
13.04.
09:45 - 13:00
Hörsaal 2i NIG 2.Stock
Wednesday
18.04.
09:45 - 13:00
Hörsaal 3B NIG 3.Stock
Friday
20.04.
09:45 - 13:00
Hörsaal 2i NIG 2.Stock
Wednesday
25.04.
09:45 - 13:00
Hörsaal 3B NIG 3.Stock
Friday
27.04.
09:45 - 13:00
Hörsaal 2i NIG 2.Stock
Friday
04.05.
09:45 - 13:00
Hörsaal 2i NIG 2.Stock
Information
Aims, contents and method of the course
Assessment and permitted materials
Preparation of literature
Seminar presentation
Contribution to discussions during the seminar
Paper on the literature and critical review within the deadline and formal constraints provided
Seminar presentation
Contribution to discussions during the seminar
Paper on the literature and critical review within the deadline and formal constraints provided
Minimum requirements and assessment criteria
Assessment uses a points based scheme:
- presence and participation during the seminar sessions 0-15 points
- literature search, thorough and critical reading of the texts, preparatory meeting and discussion, presentation (0-35 points)
- timely delivery of a final seminar paper (necessary condition) 0-50 pointsGrading will be according the points achieved: 100-90: sehr gut (1), 89-80: gut (2), 79-70: befriedigend (3), 69-60: genügend (4), 59-0: nicht genügend (5).
- presence and participation during the seminar sessions 0-15 points
- literature search, thorough and critical reading of the texts, preparatory meeting and discussion, presentation (0-35 points)
- timely delivery of a final seminar paper (necessary condition) 0-50 pointsGrading will be according the points achieved: 100-90: sehr gut (1), 89-80: gut (2), 79-70: befriedigend (3), 69-60: genügend (4), 59-0: nicht genügend (5).
Examination topics
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
Reading list
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
Association in the course directory
Last modified: Mo 07.09.2020 15:36
As a result of the seminar students should be able to search for relevant literature on- and offline, review selected literature on important basic philosophical issues in Cognitive Science, critically review and compare the literature, put it in historical conext, summarize, present, and discuss it.Learning Outcomes:
" 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
" Ability to find, read, present, and discuss primary scientific literature
" Ability to sharpen/focus/channel analytical and critical thinking
" Ability to reflect upon personal competences and develop individual motivation and interests