"openbis_all/source/git@sissource.ethz.ch:sispub/openbis.git" did not exist on "9d5a44b15b2144739a5dfff42a751939b2b9791c"
Newer
Older
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is machine learning ?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Discipline in the overlap of computer science and statistics\n",
"- Learn models from data\n",
"- Term \"Machine Learning\" was first used in 1959 by AI pioneer Arthur Samuel\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So the field is not as new as one might think, but due to more available data, processing power and development of better algorithms more applications of machine learning appeared during the last 15 years."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## About models\n",
" 1. Will the sun shine tomorrow ?\n",
" 2. Where on the night sky will I see the moon tonight ?\n",
" 2. Is the email I received spam ? \n",
" 4. What article X should I recommend to my customers Y ?\n",
"The first two questions can be answered based on existing mathematically explicit models (formulas). \n",
"\n",
"For the questions 3 and 4 it is difficult to develop explicitly formulated models. \n",
"\n",
"These problems 3 and 4 have the following in common:\n",
"- Vague understanding of the problem domain\n",
"- Enough data with sufficient (implicit) information available\n",
"\n",
"E.g. for the spamming example:\n",
"\n",
"- We have no explicit formula for such a task\n",
"- We know that specific words are specific for spam emails, other words are specific for my personal and job emails.\n",
"- My mailbox is full with examples for spam vs non-spam.\n",
"\n",
"\n",
"**In such cases machine learning offers approaches to build models based on example data.**\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some history\n",
"\n",
"\n",
" \n",
" 1812: Bayes Theorem\n",
" 1913: Markov Chains\n",
" 1951: First neural network\n",
" 1969: Book \"Perceptrons\": Limitations of Neural Networks\n",
" 1986: Backpropagation to learn neural networks\n",
" 1995: Randomized Forests and Support Vector Machines\n",
" 1998: Naive Bayes Classifier for Spam detection\n",
" 2000+: Deep learning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Features\n",
"\n",
"(Almost) all machine learning algorithms require that your data is numerical. In some applications it is not obvious how to transform data to a numerical presentation.\n",
"In most cases we can arange our data as a matrix:\n",
"- every row of such a matrix is called a **sample** or **feature vector**. \n",
"- every column name is called a **feature name** or **attribute**.\n",
"- the cells are **feature values**."
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
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>alcohol_content</th>\n",
" <th>bitterness</th>\n",
" <th>darkness</th>\n",
" <th>fruitiness</th>\n",
" <th>is_yummy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.739295</td>\n",
" <td>0.422503</td>\n",
" <td>0.989463</td>\n",
" <td>0.215791</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.207849</td>\n",
" <td>0.841668</td>\n",
" <td>0.928626</td>\n",
" <td>0.380420</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.709494</td>\n",
" <td>0.322037</td>\n",
" <td>5.374682</td>\n",
" <td>0.145231</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.684743</td>\n",
" <td>0.434315</td>\n",
" <td>4.072805</td>\n",
" <td>0.191321</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.148710</td>\n",
" <td>0.570586</td>\n",
" <td>1.461568</td>\n",
" <td>0.260218</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" alcohol_content bitterness darkness fruitiness is_yummy\n",
"0 3.739295 0.422503 0.989463 0.215791 0\n",
"1 4.207849 0.841668 0.928626 0.380420 0\n",
"2 4.709494 0.322037 5.374682 0.145231 1\n",
"3 4.684743 0.434315 4.072805 0.191321 1\n",
"4 4.148710 0.570586 1.461568 0.260218 0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"features = pd.read_csv(\"beers.csv\")\n",
"features.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This table holds five samples.\n",
"\n",
"The feature names are `alcohol_content`, `bitterness`, `darkness`, `fruitiness` and `is_yummy`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Images -> Feature vectors\n",
"\n",
"Computers represent images as matrices. Every cell in the matrix represents one pixel, and the value in the matrix cell its color.\n",
"\n",
"`scikit-learn` includes some example data sets which we load now:"
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import load_digits\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"dd = load_digits()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we plot the first nine digits from this data set:"
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1296x360 with 9 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"N = 9\n",
"plt.figure(figsize=(2 * N, 5))\n",
"for i, image in enumerate(dd.images[:N], 1):\n",
" plt.subplot(1, N, i)\n",
" plt.imshow(image, cmap=\"gray\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And this is the first image from the data set, it is a 8 x 8 matrix with values 0 to 15:"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8, 8)\n",
"[[ 0. 0. 5. 13. 9. 1. 0. 0.]\n",
" [ 0. 0. 13. 15. 10. 15. 5. 0.]\n",
" [ 0. 3. 15. 2. 0. 11. 8. 0.]\n",
" [ 0. 4. 12. 0. 0. 8. 8. 0.]\n",
" [ 0. 5. 8. 0. 0. 9. 8. 0.]\n",
" [ 0. 4. 11. 0. 1. 12. 7. 0.]\n",
" [ 0. 2. 14. 5. 10. 12. 0. 0.]\n",
" [ 0. 0. 6. 13. 10. 0. 0. 0.]]\n"
]
}
],
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To transform such an image to a feature vectore we just have to concatenate the rows to one single vector of size 64:"
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(64,)\n",
"[ 0. 0. 5. 13. 9. 1. 0. 0. 0. 0. 13. 15. 10. 15. 5. 0. 0. 3.\n",
" 15. 2. 0. 11. 8. 0. 0. 4. 12. 0. 0. 8. 8. 0. 0. 5. 8. 0.\n",
" 0. 9. 8. 0. 0. 4. 11. 0. 1. 12. 7. 0. 0. 2. 14. 5. 10. 12.\n",
" 0. 0. 0. 0. 6. 13. 10. 0. 0. 0.]\n"
]
}
],
"source": [
"vector = dd.images[0].flatten()\n",
"print(vector.shape)\n",
"print(vector)"
]
},
{
"cell_type": "markdown",
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
"### Textual data -> Feature vector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To transform some text into a feature vector, we first need a enumerated dictionary. Such a dictionary can be very large, but for the sake of simplicity we use a very small dictionary to explain the overall procedure:\n",
"\n",
"\n",
"| Word | Index |\n",
"|----------|-------|\n",
"| like | 0 |\n",
"| dislike | 1 |\n",
"| american | 2 |\n",
"| italian | 3 |\n",
"| beer | 4 |\n",
"| pizza | 5 |\n",
"\n",
"To \"vectorize\" a given text we count the words in the text which also exist in the vocabulary and put the counts at the given position `Index`.\n",
"\n",
"E.g. `\"I dislike american pizza, but american beer is nice\"`:\n",
"\n",
"| Word | Index | Count |\n",
"|----------|-------|-------|\n",
"| like | 0 | 1 |\n",
"| dislike | 1 | 1 |\n",
"| american | 2 | 2 |\n",
"| italian | 3 | 0 |\n",
"| beer | 4 | 1 |\n",
"| pizza | 5 | 1 |\n",
"\n",
"So this text can be encoded as the word vector\n",
"\n",
"`[0, 1, 2, 0, 1, 1]`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And this is how we can compute such a word vector using Python:"
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
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 0 1 1]\n"
]
}
],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from itertools import count\n",
"\n",
"# map words to index in created vector:\n",
"vocabulary = [\"like\", \"dislike\", \"american\", \"italian\", \"beer\", \"pizza\"]\n",
"\n",
"vectorizer = CountVectorizer(vocabulary=dict(zip(vocabulary, count())))\n",
"\n",
"# crate count vector for a pice of text:\n",
"vector = vectorizer.fit_transform([\"I dislike american pizza. But american beer is nice\"]).toarray()[0]\n",
"print(vector)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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
"## Taxonomy of machine learning\n",
"\n",
"We can separate learning problems in a few large categories: **supervised** and **unsupervised** learning.\n",
"\n",
"In **supervised learning** the the data comes with additional attributes that we want to predict. Such a problem can be either \n",
"\n",
"- **classification**: samples belong to two or more discrete classes and we want to learn from already labeled data how to predict the class of unlabeled data. \n",
" \n",
"- **regression**: if the desired output consists of one or more continuous variables, then the task is called regression.\n",
" \n",
" \n",
"\n",
"Examples for supervised learning:\n",
"\n",
"- Classification: Predict the class `is_yummy` based on the attributes `alcohol_content`,\t`bitterness`, \t`darkness` and `fruitiness`. (two class problem).\n",
"\n",
"- Classification: predict the digit-shown based on a 8 x 8 pixel image (this is a multi-class problem).\n",
"\n",
"- Regression: Predict the length of a salmon based on its age and weight.\n",
"\n",
"\n",
"In **unsupervised learning**, in which the training data consists of samples without any corresponding target values, one tries to find structure in data. Common applications are\n",
"\n",
"- Clustering \n",
"- Density estimation\n",
"- Dimension reduction (PCA, ...)\n",
"\n",
"This course will only introduce concepts and methods from **supervised learning**."
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
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/uweschmitt/Projects/machinelearning-introduction-workshop/venv3.6/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: get_ipython_dir has moved to the IPython.paths module since IPython 4.0.\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"text/html": [
"<style>\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
" @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Lato');\n",
" @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
"\n",
" \n",
" body {\n",
" font-family: 'Lora', Consolas, sans-serif;\n",
" \n",
" -webkit-print-color-adjust: exact important !;\n",
" \n",
" }\n",
" .rendered_html code\n",
" {\n",
" color: black;\n",
" background: #eaf0ff;\n",
" \n",
" padding: 1pt;\n",
" font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
" }\n",
" \n",
" .CodeMirror pre {\n",
" font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
" }\n",
" \n",
" .cm-s-ipython span.cm-keyword {\n",
" font-weight: normal;\n",
" }\n",
" \n",
" strong {\n",
" background: #ffe7e7;\n",
" padding: 1pt;\n",
" }\n",
" \n",
" \n",
" div #notebook {\n",
" # font-size: 10pt; \n",
" line-height: 145%;\n",
" }\n",
" \n",
" li {\n",
" line-heigt: 145%;\n",
" }\n",
"\n",
" div.output_area pre {\n",
" background: #fff9d8 !important;\n",
" padding: 5pt;\n",
" \n",
" -webkit-print-color-adjust: exact; \n",
" \n",
" }\n",
" \n",
" \n",
" \n",
" h1, h2, h3, h4 {\n",
" font-family: Kameron, arial;\n",
" }\n",
" \n",
" div#maintoolbar {display: none !important;}\n",
" </style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#REMOVEBEGIN\n",
"# THE LINES BELOW ARE JUST FOR STYLING THE CONTENT ABOVE !\n",
"\n",
"from IPython import utils\n",
"from IPython.core.display import HTML\n",
"import os\n",
"def css_styling():\n",
" \"\"\"Load default custom.css file from ipython profile\"\"\"\n",
" base = utils.path.get_ipython_dir()\n",
" styles = \"\"\"<style>\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
" @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Lato');\n",
" @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
" \n",
" @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
"\n",
" \n",
" body {\n",
" font-family: 'Lora', Consolas, sans-serif;\n",
" \n",
" -webkit-print-color-adjust: exact important !;\n",
" \n",
" }\n",
" .rendered_html code\n",
" {\n",
" color: black;\n",
" background: #eaf0ff;\n",
" \n",
" padding: 1pt;\n",
" font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
" }\n",
" \n",
" .CodeMirror pre {\n",
" font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
" }\n",
" \n",
" .cm-s-ipython span.cm-keyword {\n",
" font-weight: normal;\n",
" }\n",
" \n",
" strong {\n",
" background: #ffe7e7;\n",
" padding: 1pt;\n",
" }\n",
" \n",
" \n",
" div #notebook {\n",
" # font-size: 10pt; \n",
" line-height: 145%;\n",
" }\n",
" \n",
" li {\n",
" line-heigt: 145%;\n",
" }\n",
"\n",
" div.output_area pre {\n",
" background: #fff9d8 !important;\n",
" padding: 5pt;\n",
" \n",
" -webkit-print-color-adjust: exact; \n",
" \n",
" }\n",
" \n",
" \n",
" \n",
" h1, h2, h3, h4 {\n",
" font-family: Kameron, arial;\n",
" }\n",
" \n",
" div#maintoolbar {display: none !important;}\n",
" </style>\"\"\"\n",
" return HTML(styles)\n",
"css_styling()\n",
"#REMOVEEND"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}