Quick Sort Example Scooby Scooby Itchy Itchy Scratchy Scratchy Huckleberry Huckleberry Tom Tom Jerry Jerry Snoopy Snoopy Sylvester Sylvester Speedy Speedy Goofy Goofy Garfield Garfield Mickey Mickey Mickey Mickey Itchy Itchy Garfield Garfield Huckleberry Huckleberry Goofy Goofy Jerry Jerry Scooby Scooby Sylvester Sylvester Speedy Speedy Snoopy Snoopy Tom Tom Scratchy Scratchy Mickey Mickey Itchy Itchy Garfield Garfield Huckleberry Huckleberry Goofy Goofy Jerry Jerry Scooby Scooby Sylvester Sylvester Speedy Speedy Snoopy Snoopy Tom Tom Scratchy Scratchy Jerry Jerry Itchy Itchy Garfield Garfield Huckleberry Huckleberry Goofy Goofy Mickey Mickey Scooby Scooby Sylvester Sylvester Speedy Speedy Snoopy Snoopy Tom Tom Scratchy Scratchy Jerry Jerry Itchy Itchy Garfield Garfield Huckleberry Huckleberry Goofy Goofy Mickey Mickey Scooby Scooby Sylvester Sylvester Speedy Speedy Snoopy Snoopy Tom Tom Scratchy Scratchy Goofy Goofy Itchy Itchy Garfield Garfield Huckleberry Huckleberry Jerry Jerry Mickey Mickey Scooby Scooby Sylvester Sylvester Speedy Speedy Snoopy Snoopy Tom Tom Scratchy Scratchy Goofy Goofy Itchy Itchy Garfield Garfield Huckleberry Huckleberry Jerry Jerry Mickey Mickey Scooby Scooby Sylvester Sylvester Speedy Speedy Snoopy Snoopy Tom Tom Scratchy Scratchy Garfield Garfield Goofy Goofy Itchy Itchy Huckleberry Huckleberry Jerry Jerry Mickey Mickey Scooby Scooby Sylvester Sylvester Speedy Speedy Snoopy Snoopy Tom Tom Scratchy Scratchy Garfield Garfield Goofy Goofy Itchy Itchy Huckleberry Huckleberry Jerry Jerry Mickey Mickey Scooby Scooby Sylvester Sylvester Speedy Speedy Snoopy Snoopy Tom Tom Scratchy Scratchy Garfield Garfield Goofy Goofy Itchy Itchy Huckleberry Huckleberry Jerry Jerry Mickey Mickey Scooby Scooby Sylvester Sylvester Speedy Speedy Snoopy Snoopy Tom Tom Scratchy Scratchy Garfield Garfield Goofy Goofy Huckleberry Huckleberry Itchy Itchy Jerry Jerry Mickey Mickey Scooby Scooby Sylvester Sylvester Speedy Speedy Snoopy Snoopy Tom Tom Scratchy Scratchy Garfield Garfield Goofy Goofy Huckleberry Huckleberry Itchy Itchy Jerry Jerry Mickey Mickey Scooby Scooby Sylvester Sylvester Speedy Speedy Snoopy Snoopy Tom Tom Scratchy Scratchy Garfield Garfield Goofy Goofy Huckleberry Huckleberry Itchy Itchy Jerry Jerry Mickey Mickey Scooby Scooby Sylvester Sylvester Speedy Speedy Snoopy Snoopy Tom Tom Scratchy Scratchy Garfield Garfield Goofy Goofy Huckleberry Huckleberry Itchy Itchy Jerry Jerry Mickey Mickey Scooby Scooby Scratchy Scratchy Speedy Speedy Snoopy Snoopy Sylvester Sylvester Tom Tom Garfield Garfield Goofy Goofy Huckleberry Huckleberry Itchy Itchy Jerry Jerry Mickey Mickey Scooby Scooby Scratchy Scratchy Speedy Speedy Snoopy Snoopy Sylvester Sylvester Tom Tom Garfield Garfield Goofy Goofy Huckleberry Huckleberry Itchy Itchy Jerry Jerry Mickey Mickey Scooby Scooby Scratchy Scratchy Speedy Speedy Snoopy Snoopy Sylvester Sylvester Tom Tom Garfield Garfield Goofy Goofy Huckleberry Huckleberry Itchy Itchy Jerry Jerry Mickey Mickey Scooby Scooby Scratchy Scratchy Speedy Speedy Snoopy Snoopy Sylvester Sylvester Tom Tom Garfield Garfield Goofy Goofy Huckleberry Huckleberry Itchy Itchy Jerry Jerry Mickey Mickey Scooby Scooby Scratchy Scratchy Snoopy Snoopy Speedy Speedy Sylvester Sylvester Tom Tom Garfield Garfield Goofy Goofy Huckleberry Huckleberry Itchy Itchy Jerry Jerry Mickey Mickey Scooby Scooby Scratchy Scratchy Snoopy Snoopy Speedy Speedy Sylvester Sylvester Tom Tom Garfield Garfield Goofy Goofy Huckleberry Huckleberry Itchy Itchy Jerry Jerry Mickey Mickey Scooby Scooby Scratchy Scratchy Snoopy Snoopy Speedy Speedy Sylvester Sylvester Tom Tom CS 340 Note that a more strategic choice for each new pivot position may be Page 18 possible.
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What should we do? 1. Be smart about how we estimate probabilities from sparse data • Maximum likelihood estimates (ML) • Maximum a posteriori estimates (MAP) 2. Be smart about how to represent joint distributions • Bayes network, graphical models (more on this later) Slide credit: Tom Mitchell
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Image Credits • Slide 1 Description: Caterpillar cartoon. Credit: Alice Vacca Clearance: Licensed from Fotolia, ID#79041625. • Slide 3 (right, bottom) Description: Photo of shelter building silver spotted skipper. Credit: Laurel C. Cepero, case author. • Slide 2 (left) Description: Photo of dead caterpillar. Credit: Teles Source: Wikimedia Commons, https://commons.wikimedia.org/wiki/File:Dead_caterpillar.JPG Clearance: Public domain. • Slide 4 Description: Photo of Epargyraeus clarus adult Credit: Laurel C. Cepero, case author. • Slide 2 (right) Description: Photo of ants and caterpillar. Credit: Paulo Oliveira Clearance: Used with permission. • Slide 2 (center) Description: Photo of caterpillar on wet leaf. Source: Wikimedia Commons, https://commons.wikimedia.org/wiki/File:Dysphania_percota_cate rpillar.jpg Credit: AshLin Clearance: Used in accordance with CC BY-SA 3.0. • Slide 3 (left) Description: Photo of shelter building silver spotted skipper. Credit: Laurel C. Cepero, case author. • Slide 5 Description: Image capture from video of frass flinging. Credit: Kylee Grenis, case author. Source: https://youtu.be/hIwhUwXk4yo • Slides 6, 8, 9 10 Description: Figures and tables from Weiss paper. Credit: M.R. Weiss. Source: Good housekeeping: Why do shelterdwelling caterpillars fling their frass? Ecology Letters 6(4), 361–370. Clearance: Used with permission of Wiley and Sons. • Slide 11 Description: Photo of Dr. Weiss. Source: http://www.weisslab.org/people.html Clearance: Used with permission. • Slide 3 (right, top) Description: Photo of an opened oak leaf shelter. Credit: Kylee Grenis, case author. 12
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Dot Gaps - Sparse Ends (DG-SE) 1. Check Dotp,d(y) gaps (grids of p and d?). 1.1 Check distances at sparse ends. Analyzing the thinning at [8,9]: 7 7 7 7 7 7 7 7 7 7 7 10 10 10 10 10 10 10 10 10 10 10 10 e21i4 i8 i9 i17i24i26i27i28i38i50e2 e3 e12e5 e17e19e23e29e35e37i20i34 e21 0 9 21 15 9 6 18 5 3 9 4 7 11 7 8 6 11 9 5 7 9 11 7 i4 9 0 12 6 2 7 10 8 7 2 6 12 10 15 11 13 13 9 12 15 10 10 6 i8 21 12 0 9 11 17 4 19 18 12 18 21 15 25 19 25 22 18 22 26 17 20 16 i9 15 6 9 0 6 10 9 12 12 7 12 15 11 19 13 18 15 10 16 19 13 11 9 i17 9 2 11 6 0 7 9 8 7 1 7 11 8 15 10 14 13 9 12 15 9 11 6 i24 6 7 17 10 7 0 15 2 4 7 5 7 8 9 5 9 7 4 6 11 7 7 4 i2618 10 4 9 9 15 0 16 16 9 16 17 12 22 16 22 21 16 20 24 14 19 14 i27 5 8 19 12 8 2 16 0 2 8 5 6 8 8 5 8 7 4 5 9 7 7 4 i28 3 7 18 12 7 4 16 2 0 7 3 6 9 8 6 7 9 6 5 9 7 9 5 i38 9 2 12 7 1 7 9 8 7 0 6 10 8 14 10 13 14 9 11 14 9 11 6 i50 4 6 18 12 7 5 16 5 3 6 0 9 11 9 9 7 11 7 7 8 9 9 5 e2 7 12 21 15 11 7 17 6 6 10 9 0 6 6 4 8 10 8 5 10 4 12 7 e3 11 10 15 11 8 8 12 8 9 8 11 6 0 12 6 14 12 8 10 16 3 13 7 e12 7 15 25 19 15 9 22 8 8 14 9 6 12 0 7 4 9 9 3 6 9 11 10 e5 8 11 19 13 10 5 16 5 6 10 9 4 6 7 0 9 7 5 5 11 4 9 5 e17 6 13 25 18 14 9 22 8 7 13 7 8 14 4 9 0 10 9 4 2 11 10 9 e1911 13 22 15 13 7 21 7 9 14 11 10 12 9 7 10 0 5 7 11 10 5 9 e23 9 9 18 10 9 4 16 4 6 9 7 8 8 9 5 9 5 0 6 11 7 4 4 e29 5 12 22 16 12 6 20 5 5 11 7 5 10 3 5 4 7 6 0 6 8 9 7 e35 7 15 26 19 15 11 24 9 9 14 8 10 16 6 11 2 11 11 6 0 13 11 11 e37 9 10 17 13 9 7 14 7 7 9 9 4 3 9 4 11 10 7 8 13 0 12 6 i2011 10 20 11 11 7 19 7 9 11 9 12 13 11 9 10 5 4 9 11 12 0 7 i34 7 6 16 9 6 4 14 4 5 6 5 7 7 10 5 9 9 4 7 11 6 7 0 Actual dist from each F=7 to each F=10 is >=4. F-gap from F=6 to F=11 >=4. F-gap from F=6 to F=10>=4. Separate at F=8.5 to CLUS2.1<8.5 (2 ver, 43 vir) and CLUS2.2>8.5 (44 ver, 4 vir) Dot gp>=4 Dot gp>=4 CLUS2 p=nnnn p=aaan q=xxxx q=aaax 0 2 Sparse lower end 0 3 3 2 1 3 i32 i18 i19 i23 i6 i36 F 4 1 i32 0 2 8 4 13 11 9 9 0 5 1 i18 4 3 2 0 12 10 8 10 0 7 1 i19 13 12 4 6 0 4 5 9 3 9 2 i23 11 10 5 5 4 0 3 7 3 10 1 i6 6 5 9 8 5 3 0 5 4 11 1 i36 9 10 7 11 9 7 5 0 5 12 1 i32 and i18 gap>=4 outliers 8 2 13 2 9 4 14 1 10 12 15 3 11 8 16 4 12 13 17 3 13 5 18 2 14 3 19 8 15 7 20 2 21 3 22 1 23 4 24 5 25 4 26 5 Thin area: (40 44) 27 5 36 37 37 38 38 38 38 40 42 43 44 44 45 45 45 45 45 47 28 4 So, two rounds of Dotpd(y) gap analysis yields e4 i7 e10 e31 e32 s14 i39 s16 s19 e49 s15 e44 e11 e8 s6 s34 F 29 3 e4 0 9 5 3 4 34 24 34 29 11 35 9 8 10 29 34 37 30 2 i7 9 0 8 11 12 38 30 39 35 16 40 14 13 14 34 39 37 CLUS1 (50 Setosa, plus 4 Versicolor) 31 2 e10 5 8 0 5 6 32 23 31 27 10 33 8 9 8 27 32 38 32 2 e31 3 11 5 0 1 32 23 31 27 9 32 7 7 8 27 31 38 33 4 CLUS2.1 (43 Virginica, plus 2 Versicolor) e32 4 12 6 1 0 31 22 30 25 8 31 6 7 7 26 30 38 34 5 s14 34 38 32 32 31 0 25 20 17 23 18 26 28 25 16 17 38 36 3 CLUS2.2 (44 Veriscolor, plus 4 Virginica) i39 24 30 23 23 22 25 0 20 17 17 20 21 24 21 18 21 40 37 2 s16 34 39 31 31 30 20 20 0 6 26 5 29 33 29 6 4 42 38 4 s19 29 35 27 27 25 17 17 6 0 21 6 24 27 24 3 5 43 and picks out 3 Virginica, 4 Setosa as outliers 40 1 e49 11 16 10 9 8 23 17 26 21 0 26 4 7 4 21 25 44 42 1 s15 35 40 33 32 31 18 20 5 6 26 0 29 33 29 7 4 44 (More outliers would result by applying 1.1 to the 43 1 e44 9 14 8 7 6 26 21 29 24 4 29 0 4 1 24 28 45 44 2 e11 8 13 9 7 7 28 24 33 27 7 33 4 0 5 27 32 45 45 5 sparse ends of the 2nd round?). e8 10 14 8 8 7 25 21 29 24 4 29 1 5 0 23 28 45 47 6 s6 29 34 27 27 26 16 18 6 3 21 7 24 27 23 0 5 45 48 3 Round1: p=nnnn (n=min) and q=xxxx (x=max) s34 34 39 32 31 30 17 21 4 5 25 4 28 32 28 5 0 45 49 4 So i39,s16,s19,s49,s15 are "thin area" outliers AND s14 is also. 50 6 Round2: p=aaan (a=avg) and q=aaax 51 4 Separate at 42, giving CLUS1<41 (50 Setosa, 4 Versicolor, e8,e11,e44,e49) and CLUS2>=41. 52 3 53 5 54 3 55 5 56 1 Sparse Ends analysis should accomplish the same outlier detection that a few steps of 57 1 Sparse upper end 58 2 s43 s9 s39 s42 s14 F SL accomplishes. If an outlier is surrounded at a fixed distance then those neighbors 59 1 s23s23 5 8 7 13 7 56 60 1 s43 0 5 0 3 2 9 3 57 will show up as sparse end neighbors and the outlier-ness of the point will be detected s9 8 3 0 1 6 3 58 s39 7 2 1 0 7 2 58 by looking at pairwise distances of that sparse end. s42 13 9 6 7 0 8 59 s14 7 3 3 2 8 0 60 no gap>4 outliers
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Table of Contents Course topics are hyper-linked to respective slides. If desired click on topics to quick-jump to that section. Note: Must be in Presentation mode to use hyper-link functionality (Press F5 to begin). What is SAP Goods Receiving? Slide 3 Who Should Receive SAP Receiving Training Slide 4 SAP Roles Slide 5 Role Combinations Slide 6 Training Requirements for Departmental Roles SAP General Process Flow Slide 8 Understanding Transaction Codes Slide 9 Goods Receiving Overview Slide 10 How to Identify PO Number Slide 11 Begin Goods Receiving Slide 12 Goods Receipt Major Sections Slide 16 Create Goods Receipt Slide 21 Display Goods Receipt Slide 27 Create Partial Goods Receipt Slide 28 Cancel Goods Receipt Slide 32 Display Purchase Order from Goods Receipt Slide 37 How Determine Assigned Purchasing Buyer Slide 38 Numbering Conventions for Purchase Orders Slide 39 Setting Delivery Complete Indicator Slide 40 Create Goods Receipt Against ”Reverse” PO’s Slide 41 Create Attachment Slide 42 Naming Convention for Attachments Slide 43 Helpful Icons within MIGO Slide 44 Obtaining Hard Copy of Goods Receipt Slide 45 SAP Goods Receiving Changes to PO Quantities Slide 46 Slide 7 2
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