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課程名稱 |
腦理論
BRAIN THEORY |
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開課學期 |
97-2 |
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授課對象 |
電機資訊學院 資訊工程學研究所 |
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授課教師 |
劉長遠 |
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課號 |
CSIE7434 |
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課程識別碼 |
922 U0100 |
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班次 |
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學分 |
3 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期一6,7,8(13:20~16:20) |
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上課地點 |
資310 |
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備註 |
碩、博曾修類神經網路
限學士班四年級以上
總人數上限：30人 |
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課程網頁 |
http://red.csie.ntu.edu.tw/BT/index_eng.php |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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課程概述 |
The syllabus spans the history of the brain theory since its inception. Brain theory is the endeavor to understand mind (thinking, intellect) in terms of its design (how it is built, how it works). Subjects include: neurobiological modeling (Hebbian synapse and Hebbian learning; NMDA/LTE)
, pception and associative memory, computational mental process (Longuet-Higgins, H.C.). |
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課程目標 |
The syllabus spans the history of the brain theory since its inception. Brain theory is the endeavor to understand mind (thinking, intellect) in terms of its design (how it is built, how it works). Subjects include: neurobiological modeling (Hebbian synapse and Hebbian learning; NMDA/LTE)
, pception and associative memory, computational mental process (Longuet-Higgins, H.C.).
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課程要求 |
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
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參考書目 |
部分參考書目如下：
[1] Unsupervised Learning, H.B. Barlow, Neural Computation 1, 295-311 (1989)
[2] Finding Minimum Entropy Codes, H.B. Barlow, T.P. Kaushal, G.J. Mitchison, Neural Computation 1, 412-423(1989)
[3] A global geometric framework for nonlinear dimensionality reduction Joshua B. Tenenbaum, Vin de Silva, John C. Langford, Science, vol. 290, 22 December 2000, 2319-2323
[4] Nonlinear Dimensionality reduction by locally linear embedding, Sam T. Roweis and Lawrence K. Saul, Science, vol. 290. 22 December 2000, 2323-2326
[5] The manifold ways of perception, H. Sebastian Seung and Daniel D. Lee,
Science, vol 290, 22 December, 2268-2269
[6] Minimization of Boolean complexity in human concept learning
Jacob Feldman, Nature, vol 407, 5 October 2000, 630-632
[7] Mind design II, edited by John Haugeland, 1997
[8] Concepts for Neural Networks. A Survey edited by L.J. Landau and J.G. Taylor.
[9, 主要教科書] Reinforcement learning: An introduction, by R.S. Sutton and A.G. Barto, 1998, MIT Press.
[10] Neurocomputing: Foundations of Research, edited by James A. Anderson and Edward Rosenfeld, The MIT Press, 1988
[11, 參考教科書] Neural networks, a comprehensive foundation, second edition, by Simon Haykin, Prentice-Hall, Inc., 1999
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評量方式
(僅供參考) |
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