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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LEC# |
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READINGS |
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DUE |
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1 |
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DHS Chap. 1, A.1-A.2 |
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2 |
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DHS Chap. A.2-A.4 |
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3 |
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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 Chap. 2.5-2.6 |
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5 |
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DHS Chap. 2.8.3, 2.11. Breese & Ball Handout (for example of an application), Independence Diagram handout (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 Chap. 2.9, 3.1-3.2 |
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7 |
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DHS Chap. 3.3-3.4 |
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8 |
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DHS Chap. 3.5, 3.7-3.8, Belhumeur et. al. paper |
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9 |
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DHS Chap. 3.8-3.9 |
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10, 11 |
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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 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 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 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 Chap. 8.3-8.4, Chap 10.4.3, 10.6-10.10 |
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20 |
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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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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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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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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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Project Presentations: Face and Music/Artist data sets |
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All presentations are due online |
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25 |
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Project Presentations: PAF and special topics. |
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