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閱讀下列日曆表中的檔需要Zip程式(如Winzip)和Postscript閱讀程式(如Ghostviewer)。
In order to read all of the files listed in the calendar, a zip program such as Winzip and a postscript viewer such as Ghostviewer are necessary.
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課 |
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閱讀資料 |
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到期日 |
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1 |
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DHS:章節A.1-A.2 DHS Chap. 1, A.1-A.2 |
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2 |
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DHS:章節A.2-A.4 DHS Chap. A.2-A.4 |
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3 |
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DHS:章節A.5,2.1-2.4(可以跳過章節2.3.1,2.3.2) DHS Chap. A.5, 2.1-2.4 (can skip 2.3.1, 2.3.2) |
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4 |
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DHS:章節2.5-2.6 DHS Chap. 2.5-2.6 |
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5 |
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DHS:章節 2.8.3, 2.11。Breese & Ball 講義(該講義提供了一個應用實例),獨立圖講義(請下載並閱讀,我們可能會在後期的課程中回顧該講義),Cowell的文章(該文中討論的關於貝葉斯網路的內容超出了我們課程涵蓋的範圍,可它是在DHS的書之外的很好的關於貝葉斯網路的介紹。請閱讀9-18頁,並通讀其餘的內容,這樣你們將知道該文章涉及了哪些內容以便將來參考)。
DHS Chap. 2.8.3, 2.11.Breese & Ball Handout (for example of an application), Independence Diagram handout (pls. download and at least read this lightly now; we will probably revisit it later in the course as well), Cowell article (This goes into more on Bayes Nets than we will cover, but is a good introduction that goes beyond DHS. Pls read pp. 9-18 and give at least a quick glance at the rest, so you'll know what other topics it covers for possible future reference.) |
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6 |
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DHS:章節2.9,3.1-3.2 DHS Chap. 2.9, 3.1-3.2 |
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7 |
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DHS:章節3.3-3.4 DHS Chap. 3.3-3.4 |
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8 |
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DHS:章節3.5,3.7-3.8,Belhumeue等人的文章 DHS Chap. 3.5, 3.7-3.8, Belhumeur et. al. paper |
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9 |
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DHS:章節3.8-3.9 DHS Chap. 3.8-3.9 |
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10, 11 |
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Rabiner,Juang:章節6.1-6.5,(DHS:章節3.10,可選) Rabiner, and Juang. 6.1-6.5 and 6.12, DHS Chap. 3.10 (optional) |
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13 |
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DHS:章節4.1-4.4, 4.5,第177-178頁 和 4.5.4, 4.6.1
DHS Chap. 4.1-4.4, 4.5 pp 177-178 and 4.5.4, 4.6.1 |
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14, 15 |
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DHS:章節5.1-5.5.1, 5.8-5.8.3, 5.11, 6.1 DHS Chap. 5.1-5.5.1, 5.8-5.8.3, 5.11, 6.1 |
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16, 17 |
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DHS:章節6.2-6.3, 8.1-8.2, 10.1-10.4.2 DHS Chap 6.2-6.3, 8.1-8.2, 10.1-10.4.2 |
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18, 19 |
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DHS:章節8.3-8.4, 10.4.3, 10.6-10.10 DHS Chap. 8.3-8.4, Chap 10.4.3, 10.6-10.10 |
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20 |
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DHS:章節9,和《電子選舉,我們使用了錯誤的投票機制嗎?》科技新聞2002年11月2日。
DHS Chap 9, and "Election Selection: Are we using the worst voting procedure?" Science News, Nov. 2 2002. |
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21 |
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客座講師 Yuan Qi的講稿, Jordan, 和 Bishop.的書中涉及卡爾曼濾波的第十四章。在Tom Minka的短文中將該章節內容與隱馬爾可夫模型(HMM)相聯繫。
Guest lecture by Yuan Qi. Jordan, and Bishop. Chap. 14 in Kalman Filtering. Tom Minka's short paper relating this to HMM's. |
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22 |
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客座講師Ashish Kapoor. Muller的講稿,《基於kernel的學習演算法簡介》,發表於IEEE神經網路匯刊。
Guest lecture by Ashish Kapoor. Muller et al. "An Introduction to Kernel Based Learning Algorithms" In IEEE Trans on Neural Networks. |
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23 |
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總結課:Yuan Qi介紹貝葉斯點機器和強連接樹(更多內容請參考Jordan & Bishop的書中的第16章)以及第5講義中的Cowell的文章。最後將對整個課程做一個總結。
Combined "final" lecture: Yuan Qi introduces Bayes Point Machines (Zip file) and Junction Trees (for more information see Chap. 16 of Jordan & Bishop's book) and also the Cowell article from Lecture 5. Finally, a wrap up with a brief course overview. |
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24 |
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專案專題講演:臉,音樂,藝術家資料集 Project Presentations: Face and Music/Artist data sets |
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所有講演都需要線上提交 All presentations are due online |
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25 |
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專案專題講演:PAF以及特殊主題 Project Presentations: PAF and special topics. |
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