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本頁翻譯進度

燈號說明

審定:無
翻譯:田菁(Jing Tian)(簡介並寄信)
編輯:韋東(簡介並寄信)

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.


閱讀資料 到期日
1 DHS:章節A.1-A.2
DHS Chap. 1, A.1-A.2
2 DHS:章節A.2-A.4
DHS Chap. A.2-A.4
3 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)
4 DHS:章節2.5-2.6
DHS Chap. 2.5-2.6
5 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.)
6 DHS:章節2.9,3.1-3.2
DHS Chap. 2.9, 3.1-3.2
7 DHS:章節3.3-3.4
DHS Chap. 3.3-3.4
  • 作業2 (PDF)
    Problem Set 2 (PDF)
8 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
9 DHS:章節3.8-3.9
DHS Chap. 3.8-3.9
10, 11 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)
13 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
14, 15 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
16, 17 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
18, 19 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
20 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.
21 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.
22 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.
23 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.
24 專案專題講演:臉,音樂,藝術家資料集
Project Presentations: Face and Music/Artist data sets
所有講演都需要線上提交
All presentations are due online
25 專案專題講演:PAF以及特殊主題
Project Presentations: PAF and special topics.

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