Goldsmiths' Department of Computing offers an interdisciplinary MSc in Cognitive Computing, aimed at humanities graduates. It claims to offer "a broad exploration of radical new theoretical approaches, characterised by their emphasis on embodiment, enactivism and European phenomenology". Some of the courses:
Cognitive science and its critics: This is the core module of the course and covers the history of cognitive science from the British empiricists to mind as motion; second order cybernetics and the embodied mind. You will look at: computing machinery and intelligence – the fundamentals of computing, program speedup, limitations of computing, what is a computer; the philosophy of artificial intelligence – critical review of key papers in the foundations of artificial intelligence; problems with computationalism – review of critiques by Dreyfus, Searle, Varela, Brooks, Penrose, Putnam, van Gelder etc.
Human cognition: The focus of this course is on the experimental investigation of cognition. The topics covered will include: expertise, talent, and savants; implicit and explicit memory; and face recognition and naming. The course will draw on behavioural, neuro-imaging, and neuropsychological studies, developmental approaches, computational modeling.
Topics in neuropsychology: This course covers a range of issues fundamental to developments in understanding the neuropsychology of both normal and abnormal human functioning. Specific topics will include: causes and psychological sequelae of brain injury; dysfunctions of memory, perception, language, and executive processes; neuro-imaging techniques; disorders of motivation, behaviour, and mood; neuropharmacology of cognitive dysfunction.
Technology of thought/Artificial Intelligence: This course provides an introduction to some of the ideas and techniques of artificial intelligence. The course will concentrate upon formal approaches to artificial intelligence, where logic is used as language for representation and reasoning with problems. The aim of the course is to encourage critical and analytical thinking.
Neural networks: This course introduces the theory and practice of neural computation. It provides the principles of neural computing with artificial neural networks widely used for addressing real world problems such as classification, regression, system identification, pattern recognition, data mining, time series prediction etc.
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