Dit is een conceptversie. De vakomschrijving kan nog wijzigen, bekijk deze pagina op een later moment nog eens.
Dit is een conceptversie. De vakomschrijving kan nog wijzigen, bekijk deze pagina op een later moment nog eens.
| Faculteit | Science and Engineering |
| Jaar | 2022/23 |
| Vakcode | WMCC009-05 |
| Vaknaam | Computational Simulations of Language |
| Niveau(s) | master |
| Voertaal | Engels |
| Periode | semester II a |
| ECTS | 5 |
| Rooster | rooster.rug.nl |
| Uitgebreide vaknaam | Computational Simulations of Language | ||||||||||||||||||||||||
| Leerdoelen | At the end of the course, students should be able to talk about the history of computational simulations of language learning, and understand the differences between learning models, recognizing the benefits as well as the shortcoming. Students should also be able to set up computational simulation of a statistical learning experiment when presented with experimental data. | ||||||||||||||||||||||||
| Omschrijving | This course introduces students to implicit statistical learning in language, and computational simulations of that learning. The course also gives an introduction to the debate between empirical and rational models of language. Topics covered include: 1. Connectionist models of language: their rationale, achievements and drawbacks 2. The debate between connectionism and symbolic models of cognition 3. Psychological proposals of learning applied to language, including naive discriminate learning 4. The similarities and differences in learning progression modelled with recurrent neural networks compared to modelling with naive discriminate learning. 5. Insight into what statistical language patterns can be learned implicitly by infants and adults, what statistical patterns are challenging, and what this tells us about human cognition Student evaluations are based on the results of three computer labs. In these labs students: • recreate and investigate the behavior of classic connectionist models. • recreate and investigate the behavior of naïve discriminate learning • model experimental data that illustrates statistical learning in humans, comparing naïve discriminate learning and compare these models to neural network models • evaluate the results of the simulations compared to results from human participants The course gives a gentle introduction into simple neural networks and naïve discriminant learning. Students are not expected to have previous experience with neural networks or other machine learning methods in order to do the course. |
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| Uren per week | |||||||||||||||||||||||||
| Onderwijsvorm |
Hoorcollege (LC), Practisch werk (PRC)
(Introductory lecture, homework and lab sessions) |
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| Toetsvorm | Opdracht (AST) | ||||||||||||||||||||||||
| Vaksoort | master | ||||||||||||||||||||||||
| Coördinator | Dr. J.K. Spenader | ||||||||||||||||||||||||
| Docent(en) | Dr. J.K. Spenader | ||||||||||||||||||||||||
| Entreevoorwaarden | Mandatory: No prior knowledge is assumed. Please note that the student is expected to have a relevant BSc degree. | ||||||||||||||||||||||||
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| Opgenomen in |
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