/
2013-01-25-Intro.html
887 lines (704 loc) · 22.8 KB
/
2013-01-25-Intro.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
<?xml version="1.0" encoding="utf-8"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en">
<head>
<title>2013-01-25-Intro</title>
<meta http-equiv="Content-Type" content="text/html;charset=utf-8"/>
<meta name="title" content="2013-01-25-Intro"/>
<meta name="generator" content="Org-mode"/>
<meta name="generated" content="2013-02-01 11:00:01 PST"/>
<meta name="author" content="Jim Blomo"/>
<meta name="description" content=""/>
<meta name="keywords" content=""/>
<link rel="stylesheet" type="text/css" href="production/common.css" />
<link rel="stylesheet" type="text/css" href="production/screen.css" media="screen" />
<link rel="stylesheet" type="text/css" href="production/projection.css" media="projection" />
<link rel="stylesheet" type="text/css" href="production/color-blue.css" media="projection" />
<link rel="stylesheet" type="text/css" href="production/presenter.css" media="presenter" />
<link href='http://fonts.googleapis.com/css?family=Lobster+Two:700|Yanone+Kaffeesatz:700|Open+Sans' rel='stylesheet' type='text/css'>
</head>
<body>
<div id="preamble">
</div>
<div id="content">
<h1 class="title">2013-01-25-Intro</h1>
<div id="table-of-contents">
<h2>Table of Contents</h2>
<div id="text-table-of-contents">
<ul>
<li><a href="#sec-1">1 Data Mining i290</a></li>
<li><a href="#sec-2">2 Course Goals</a></li>
<li><a href="#sec-3">3 We'll Do Stuff</a>
<ul>
<li><a href="#sec-3-1">3.1 Homework Details</a></li>
</ul>
</li>
<li><a href="#sec-4">4 But Don't Worry</a>
<ul>
<li><a href="#sec-4-1">4.1 Help</a></li>
</ul>
</li>
<li><a href="#sec-5">5 This is a Graduate class</a>
<ul>
<li><a href="#sec-5-1">5.1 Style</a></li>
</ul>
</li>
<li><a href="#sec-6">6 Prerequisites</a>
<ul>
<li><a href="#sec-6-1">6.1 Basics</a></li>
</ul>
</li>
<li><a href="#sec-7">7 Material</a>
<ul>
<li><a href="#sec-7-1">7.1 What will we learn?</a></li>
</ul>
</li>
<li><a href="#sec-8">8 Lectures & Labs</a>
<ul>
<li><a href="#sec-8-1">8.1 Helpful tips</a></li>
</ul>
</li>
<li><a href="#sec-9">9 Office Hours</a>
<ul>
<li><a href="#sec-9-1">9.1 Details</a></li>
</ul>
</li>
<li><a href="#sec-10">10 <b>Questions?</b></a></li>
<li><a href="#sec-11">11 Schedule</a></li>
<li><a href="#sec-12">12 Hi, I'm Jim Blomo</a></li>
<li><a href="#sec-13">13 Hi, I'm Shreyas</a></li>
<li><a href="#sec-14">14 Data is Important</a>
<ul>
<li><a href="#sec-14-1">14.1 Decisions</a></li>
</ul>
</li>
<li><a href="#sec-15">15 Data is Important</a>
<ul>
<li><a href="#sec-15-1">15.1 Nice example of data mining</a></li>
</ul>
</li>
<li><a href="#sec-16">16 Data Mining ecosystem</a>
<ul>
<li><a href="#sec-16-1">16.1 Ecosystem</a></li>
<li><a href="#sec-16-2">16.2 Analysis vs. Data Mining</a>
<ul>
<li><a href="#sec-16-2-1">16.2.1 Pedantic</a></li>
</ul>
</li>
<li><a href="#sec-16-3">16.3 Machine Learning</a>
<ul>
<li><a href="#sec-16-3-1">16.3.1 Uses</a></li>
</ul>
</li>
<li><a href="#sec-16-4">16.4 Probability & Statistics</a>
<ul>
<li><a href="#sec-16-4-1">16.4.1 Uses</a></li>
</ul></li>
</ul>
</li>
<li><a href="#sec-17">17 Process</a>
<ul>
<li><a href="#sec-17-1">17.1 Common Themes</a></li>
</ul>
</li>
<li><a href="#sec-18">18 <b>Break</b></a></li>
</ul>
</div>
</div>
<div id="outline-container-1" class="outline-2">
<h2 id="sec-1"><span class="section-number-2">1</span> Data Mining i290 <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-1">
<ul>
<li>Jim Blomo & Shreyas
</li>
</ul>
</div>
</div>
<div id="outline-container-2" class="outline-2">
<h2 id="sec-2"><span class="section-number-2">2</span> Course Goals <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-2">
<ul>
<li>Extract <b>information</b> from <b>data</b>
</li>
<li>Understand techniques to find patterns
</li>
<li>Apply algorithms to real data sets
</li>
</ul>
</div>
</div>
<div id="outline-container-3" class="outline-2">
<h2 id="sec-3"><span class="section-number-2">3</span> We'll Do Stuff <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-3">
<ul>
<li>30%: 10 Homework Assignments
</li>
<li>30%: 1 Midterm
</li>
<li>40%: 1 Project: Find, Mine, Report on Data
</li>
</ul>
</div>
<div id="outline-container-3-1" class="outline-3">
<h3 id="sec-3-1"><span class="section-number-3">3.1</span> Homework Details <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-3-1">
<ul>
<li>HW due at midnight Thursday before class
</li>
<li>Each 24 hours late is 10% off
</li>
<li>HW will be turned in by GitHub pull request
</li>
<li>Project will be submitted by email & presentation
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-4" class="outline-2">
<h2 id="sec-4"><span class="section-number-2">4</span> But Don't Worry <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-4">
<ul>
<li>This isn't a programming class
</li>
<li>Grades are based on understanding of the concepts, not the craziest project
</li>
<li>Shreyas & I are here to help
</li>
</ul>
</div>
<div id="outline-container-4-1" class="outline-3">
<h3 id="sec-4-1"><span class="section-number-3">4.1</span> Help <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-4-1">
<ul>
<li>We realize there's a wide range of technical skill
</li>
<li>We will help get anyone up to speed in these technical areas
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-5" class="outline-2">
<h2 id="sec-5"><span class="section-number-2">5</span> This is a Graduate class <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-5">
<ul>
<li>Perform well without supervision
</li>
<li>Readings from both book and online documentation
</li>
<li>TMTOWTDI
</li>
<li>Getting frameworks working on your computer
</li>
</ul>
</div>
<div id="outline-container-5-1" class="outline-3">
<h3 id="sec-5-1"><span class="section-number-3">5.1</span> Style <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-5-1">
<ul>
<li>More firehouse than spoon feed, you'll need to follow up for
understanding
</li>
<li>Honor system: No copying code or answers. Helping each other with
concepts is encouraged, but document it.
</li>
<li>Everybody has a different workflow. We'll be covering the most basic.
Great if you want to do something different, but realize we may not be able
to help you as much.
</li>
<li>Non ISchool students should email student ID from EDU account to shreyas and
jblomo and we will get them ischool accounts.
</li>
<li>You may want to use other frameworks for your projects. Great! But again,
we may not be familiar with them
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-6" class="outline-2">
<h2 id="sec-6"><span class="section-number-2">6</span> Prerequisites <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-6">
<ul>
<li>Basic probability: P(A), P(A or B), P(A and B), P(A | B)
</li>
<li>Basic programming: Python
</li>
<li>Basic command line: SSH, downloading, copying large files, running programs
against data
</li>
<li>Textbook: Han, J., Kamber, M., & Pei, J. (2011). <span style="text-decoration:underline;">Data Mining: Concepts and Techniques</span>, Third Edition <b>(3rd ed.)</b>. Morgan Kaufmann.
</li>
<li>Technology will be available on <code>ischool.berkeley.edu</code>
</li>
</ul>
</div>
<div id="outline-container-6-1" class="outline-3">
<h3 id="sec-6-1"><span class="section-number-3">6.1</span> Basics <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-6-1">
<ul>
<li>"Probability of A", "Probability of A or B" "A and B" "A given B"
</li>
<li>Most assignments filling in algorithm code
</li>
<li>Project you may use any language, though we suggest Python.
</li>
<li>We'll introduce any specific frameworks
</li>
<li>Command line: cp, mv, less… Imagine you have a 10G file, how are you
going to inspect the contents?
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-7" class="outline-2">
<h2 id="sec-7"><span class="section-number-2">7</span> Material <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-7">
<ul>
<li>Process: from find data to mining it to visualizing results
</li>
<li>Algorithms: all intuitively motivated, some rigorously studied
</li>
<li>Programming: using algorithms against data sets
</li>
<li>Discovery: finding information in self-defined project
</li>
</ul>
</div>
<div id="outline-container-7-1" class="outline-3">
<h3 id="sec-7-1"><span class="section-number-3">7.1</span> What will we learn? <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-7-1">
<ul>
<li>Data mining not just about algorithms. We'll learn how to obtain, clean,
and store data.
</li>
<li>In real life, this is 70% of the job!
</li>
<li>We'll cover many different algorithms, and dive in depth on several of
them. But we're not going to get into any hairy math proofs
</li>
<li>Programming is the best way to precisely describe an algorithm. It is also
the way data mining is used in the real world.
</li>
<li>Your own project should emphasize your passion. Again, real world requires
you to grab data and squeeze information out of it without external help
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-8" class="outline-2">
<h2 id="sec-8"><span class="section-number-2">8</span> Lectures & Labs <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-8">
<ul>
<li>Start with Q&A for at least 10 minutes
</li>
<li>Expect to be asked a question
</li>
<li>Breaks
</li>
<li>Lab: Stick around and get the first question of HW done
</li>
<li>Slides on <a href="http://jblomo.github.com/datamining290/">http://jblomo.github.com/datamining290/</a>
</li>
</ul>
</div>
<div id="outline-container-8-1" class="outline-3">
<h3 id="sec-8-1"><span class="section-number-3">8.1</span> Helpful tips <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-8-1">
<ul>
<li>Helpful to me if you say your name
</li>
<li>Sorry, I tend to forget names
</li>
<li>If I am not calling on you, check to make sure you are on the class list!
</li>
<li>I'm not taking attendance, but let me know if you can't make it so I
won't call on you
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-9" class="outline-2">
<h2 id="sec-9"><span class="section-number-2">9</span> Office Hours <span class="tag"><span class="slide">slide</span> <span class="two_col">two_col</span></span></h2>
<div class="outline-text-2" id="text-9">
<ul>
<li>We'll stay after class
</li>
<li>or schedule a Skype call
</li>
<li><a href="https://piazza.com/class#spring2013/i290">Piazza</a> for questions and
announcements
</li>
<li>Wait list will be processed normally until 3rd week… then I'll accept
everyone who's participated in class if we have physical room
</li>
</ul>
<p> <img src="img/Office_Hours.png" alt="img/Office_Hours.png" />
</p>
</div>
<div id="outline-container-9-1" class="outline-3">
<h3 id="sec-9-1"><span class="section-number-3">9.1</span> Details <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-9-1">
<ul>
<li>I expect that everyone will be able to get into the class
</li>
<li>img src: <a href="http://statweb.calpoly.edu/srein/">http://statweb.calpoly.edu/srein/</a>
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-10" class="outline-2">
<h2 id="sec-10"><span class="section-number-2">10</span> <b>Questions?</b> <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-10">
</div>
</div>
<div id="outline-container-11" class="outline-2">
<h2 id="sec-11"><span class="section-number-2">11</span> Schedule <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-11">
<p>Available at <a href="http://jblomo.github.com/datamining290/">GitHub Syllabus page</a>
</p><ul>
<li>Jan 25 Class Intro ; Tools Intro by <i>GUEST: Shreyas</i>
<ul>
<li>lab: Git Intro
</li>
</ul>
</li>
<li>Feb 1 Case Studies ; Obtaining Data
</li>
<li>Feb 8 Probability ; Preprocessing
</li>
<li>Feb 15 MapReduce, Data Warehouse
</li>
<li>Feb 22 Decision Trees; Naive Bayes
</li>
<li>Mar 1 SVM ; Neural Networks
</li>
<li>Mar 8 Clustering ; Review
<ul>
<li>lab: Project Proposal Due
</li>
</ul>
</li>
<li>Mar 15 <b>Midterm</b>
<ul>
<li>lab: -
</li>
</ul>
</li>
<li>Mar 21 Dimensionality Curse ; Graph Mining
</li>
<li>Mar 29 HOLIDAY
</li>
<li>Apr 5 Pattern ; Evaluations
</li>
<li>Apr 12 Collaborative Filtering; PageRank
</li>
<li>Apr 19 Feature Extraction ; Evaluation
</li>
<li>Apr 26 Images ; Audio
</li>
<li>May 3 Visualization ; HTML
</li>
<li>May 10 In Real Life ; Review
<ul>
<li>lab: -
</li>
</ul>
</li>
<li>May 17 Final Presentation
<ul>
<li>lab: Bye!
</li>
</ul>
</li>
</ul>
</div>
</div>
<div id="outline-container-12" class="outline-2">
<h2 id="sec-12"><span class="section-number-2">12</span> Hi, I'm Jim Blomo <span class="tag"><span class="slide">slide</span> <span class="two_col">two_col</span></span></h2>
<div class="outline-text-2" id="text-12">
<p>*<a href="https://www.dropbox.com/s/obnsldacg355wqn/2013-01-08 21.50.03.mp4">Hello Class!</a>*
</p>
<ul>
<li>Cal EECS
</li>
<li>A9 - Amazon Search
</li>
<li>PBworks
</li>
<li>Yelp
</li>
<li>Lecturer
</li>
</ul>
</div>
</div>
<div id="outline-container-13" class="outline-2">
<h2 id="sec-13"><span class="section-number-2">13</span> Hi, I'm Shreyas <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-13">
<ul>
<li>First year Grad Student (MIMS '14)
</li>
<li>Also TA'd Analyzing Big Data class
</li>
<li>I can be reached at <code>seekshreyas@gmail.com</code>
</li>
</ul>
</div>
</div>
<div id="outline-container-14" class="outline-2">
<h2 id="sec-14"><span class="section-number-2">14</span> Data is Important <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-14">
<ul>
<li>Making decisions is a core part of humanity
</li>
<li>Data can help you make better decisions
</li>
<li>Challenge: extract information from data to improve decisions
</li>
</ul>
</div>
<div id="outline-container-14-1" class="outline-3">
<h3 id="sec-14-1"><span class="section-number-3">14.1</span> Decisions <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-14-1">
<ul>
<li>From big to small; from planning to execution
</li>
<li>Business questions: what is the ROI of this feature? Where to concentrate
development?
</li>
<li>Personal questions: Where to eat dinner tonight? What movie to see?
</li>
<li>Improving decisions means improving quality of life
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-15" class="outline-2">
<h2 id="sec-15"><span class="section-number-2">15</span> Data is Important <span class="tag"><span class="slide">slide</span> <span class="center">center</span></span></h2>
<div class="outline-text-2" id="text-15">
<iframe width="560" height="315" src="http://www.youtube.com/embed/y7een27u1GM" frameborder="0" allowfullscreen></iframe>
</div>
<div id="outline-container-15-1" class="outline-3">
<h3 id="sec-15-1"><span class="section-number-3">15.1</span> Nice example of data mining <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-15-1">
<ul>
<li>Stop at 3:51
</li>
<li>Had to work with external parties to get data (Yelp, city of Seattle)
</li>
<li>Had to clean data (literally, sometimes he was just handed paper receipts)
</li>
<li>Used regression analysis to discover patterns
</li>
<li>created follow up questions
</li>
<li>Used result to understand the meaning behind the data
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-16" class="outline-2">
<h2 id="sec-16"><span class="section-number-2">16</span> Data Mining ecosystem <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-16">
<ul>
<li>Data mining is part of a process to make decisions from data
</li>
<li>Intersection between statistics, computer science, data management, machine
learning
</li>
<li>Analysis & visualization often required
</li>
</ul>
</div>
<div id="outline-container-16-1" class="outline-3">
<h3 id="sec-16-1"><span class="section-number-3">16.1</span> Ecosystem <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-16-1">
<ul>
<li>We'll talk about several ways to think about the process from data to
knowledge
</li>
<li>No universally agreed process, or black-and-white boundaries
</li>
<li>Analysis: used at the beginning of investigations to understand data
characteristics
</li>
<li>Visualization: better understanding of the results of analysis or data
mining
</li>
</ul>
</div>
</div>
<div id="outline-container-16-2" class="outline-3">
<h3 id="sec-16-2"><span class="section-number-3">16.2</span> Analysis vs. Data Mining <span class="tag"><span class="slide">slide</span> <span class="two_col">two_col</span></span></h3>
<div class="outline-text-3" id="text-16-2">
<ul>
<li><b>Analysis</b>: manually investigating data. No algorithms.
</li>
<li>Statistical qualities: mean, median, standard deviation
</li>
<li>Histograms (manually set buckets)
</li>
<li>Counts / Percentages
</li>
</ul>
<ul>
<li><b>Data Mining</b>: discovering patterns though automated algorithms
</li>
<li>Regressions: fitting data to a model
</li>
<li>Clustering: grouping data without manually set descriptions
</li>
<li>Classification: identifying divisive features
</li>
</ul>
</div>
<div id="outline-container-16-2-1" class="outline-4">
<h4 id="sec-16-2-1"><span class="section-number-4">16.2.1</span> Pedantic <span class="tag"><span class="notes">notes</span></span></h4>
<div class="outline-text-4" id="text-16-2-1">
<ul>
<li>Difference is subtle, but important for both the project and your resume
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-16-3" class="outline-3">
<h3 id="sec-16-3"><span class="section-number-3">16.3</span> Machine Learning <span class="tag"><span class="slide">slide</span> <span class="two_col">two_col</span></span></h3>
<div class="outline-text-3" id="text-16-3">
<ul>
<li>Programs that can learn from data
</li>
<li>Focus on prediction, based on verified training data
</li>
<li>Used in two ways: during DM, after DM
</li>
</ul>
<p> <img src="img/Terminator.jpg" alt="img/Terminator.jpg" />
</p>
</div>
<div id="outline-container-16-3-1" class="outline-4">
<h4 id="sec-16-3-1"><span class="section-number-4">16.3.1</span> Uses <span class="tag"><span class="notes">notes</span></span></h4>
<div class="outline-text-4" id="text-16-3-1">
<dl>
<dt>During</dt><dd>assume we have training data, train on it, see how useful trained
program is or find outliers
</dd>
<dt>After</dt><dd>Discover clusters, verify and label clusters. Use labelled clusters
to train a program to recognize new data points
</dd>
</dl>
</div>
</div>
</div>
<div id="outline-container-16-4" class="outline-3">
<h3 id="sec-16-4"><span class="section-number-3">16.4</span> Probability & Statistics <span class="tag"><span class="slide">slide</span> <span class="two_col">two_col</span></span></h3>
<div class="outline-text-3" id="text-16-4">
<p> <img src="img/Poisson_cdf.svg.png" alt="img/Poisson_cdf.svg.png" />
</p><ul>
<li>Data describes real world events
</li>
<li>Probability can describe real world <b>expected</b> events
</li>
<li>Distributions can be used to summarize data, understand the factors behind
its creation
</li>
</ul>
</div>
<div id="outline-container-16-4-1" class="outline-4">
<h4 id="sec-16-4-1"><span class="section-number-4">16.4.1</span> Uses <span class="tag"><span class="notes">notes</span></span></h4>
<div class="outline-text-4" id="text-16-4-1">
<ul>
<li>Can "fit" data to a distribution, find outliers that are unexpected
</li>
<li>An example: Poisson distribution describes the expectation of a particular
number of events occurring.
<ul>
<li>Eg. pieces of mail. average is 4, but it can vary. Is getting 7 or more
pieces of mail really an outlier?
</li>
</ul>
</li>
</ul>
</div>
</div>
</div>
</div>
<div id="outline-container-17" class="outline-2">
<h2 id="sec-17"><span class="section-number-2">17</span> Process <span class="tag"><span class="slide">slide</span> <span class="two_col">two_col</span></span></h2>
<div class="outline-text-2" id="text-17">
<ul>
<li><b>Knowledge Discovery in Databases (KDD)</b>
</li>
<li>Selection
</li>
<li>Pre-processing
</li>
<li>Transformation
</li>
<li>Data Mining
</li>
<li>Interpretation/Evaluation
</li>
</ul>
<ul>
<li><b>Cross Industry Standard Process for Data Mining</b>
</li>
<li>Business Understanding
</li>
<li>Data Understanding
</li>
<li>Data Preparation
</li>
<li>Modeling
</li>
<li>Evaluation
</li>
<li>Deployment
</li>
</ul>
</div>
<div id="outline-container-17-1" class="outline-3">
<h3 id="sec-17-1"><span class="section-number-3">17.1</span> Common Themes <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-17-1">
<ul>
<li>Figure out what you want to do
</li>
<li>Get the data
</li>
<li>Make sure it's OK
</li>
<li>Understanding
</li>
<li>Make a decision, test its effectiveness
</li>
<li>Reading will cover another process, aimed at "Data Science", but basically
applies to Data Mining
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-18" class="outline-2">
<h2 id="sec-18"><span class="section-number-2">18</span> <b>Break</b> <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-18">
<script type="text/javascript" src="production/org-html-slideshow.js"></script>
</div>
</div>
</div>
<div id="postamble">
<p class="date">Date: 2013-02-01 11:00:01 PST</p>
<p class="author">Author: Jim Blomo</p>
<p class="creator">Org version 7.8.02 with Emacs version 23</p>
<a href="http://validator.w3.org/check?uri=referer">Validate XHTML 1.0</a>
</div>
</body>
</html>