forked from johnmyleswhite/MLNotes
-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathoutline.html
329 lines (264 loc) · 5.06 KB
/
outline.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
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<!-- saved from url=(0014)about:internet -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<title></title>
<base target="_blank"/>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 12px;
margin: 8px;
}
tt, code, pre {
font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace;
}
h1 {
font-size:2.2em;
}
h2 {
font-size:1.8em;
}
h3 {
font-size:1.4em;
}
h4 {
font-size:1.0em;
}
h5 {
font-size:0.9em;
}
h6 {
font-size:0.8em;
}
a:visited {
color: rgb(50%, 0%, 50%);
}
pre {
margin-top: 0;
max-width: 95%;
border: 1px solid #ccc;
}
pre code {
display: block; padding: 0.5em;
}
code.r {
background-color: #F8F8F8;
}
table, td, th {
border: none;
}
blockquote {
color:#666666;
margin:0;
padding-left: 1em;
border-left: 0.5em #EEE solid;
}
hr {
height: 0px;
border-bottom: none;
border-top-width: thin;
border-top-style: dotted;
border-top-color: #999999;
}
@media print {
* {
background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
}
a, a:visited {
text-decoration: underline;
}
hr {
visibility: hidden;
page-break-before: always;
}
pre, blockquote {
padding-right: 1em;
page-break-inside: avoid;
}
tr, img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
}
h2, h3 {
page-break-after: avoid;
}
}
</style>
<!-- MathJax scripts -->
<script type="text/javascript" src="https://c328740.ssl.cf1.rackcdn.com/mathjax/2.0-latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
</head>
<body>
<p>Norms:</p>
<ul>
<li>Absolute Value</li>
<li>Euclidean Norm</li>
<li>\(L_{p}\) Norm</li>
<li>Frobenius Norm</li>
</ul>
<p>Bases:</p>
<ul>
<li>Standard Polynomial Basis</li>
<li>Chebyshev Basis</li>
<li>Fourier Basis</li>
<li>Wavelet Basis</li>
</ul>
<p>Regression:</p>
<ul>
<li>Linear Regression</li>
<li>Generalized Additive Model</li>
<li>kNN</li>
<li>Decision Trees</li>
<li>Random Forests</li>
</ul>
<p>Classification:</p>
<ul>
<li>Naive Bayes</li>
<li>Logistic Regression</li>
<li>kNN</li>
<li>Decision Trees</li>
<li>Random Forests</li>
</ul>
<p>Feature Construction:</p>
<ul>
<li>Autoencoders</li>
<li>Deep Learning</li>
</ul>
<p>Text Analysis:</p>
<ul>
<li>Bag of Words</li>
<li>TF-IDF</li>
<li>LDA</li>
<li>LSA</li>
<li>pLSA</li>
<li>Federalist Papers Analysis</li>
</ul>
<p>Dimensionality Reduction:</p>
<ul>
<li>PCA</li>
<li>ICA</li>
<li>NMF</li>
</ul>
<p>Regularization:</p>
<ul>
<li>L2</li>
<li>L1</li>
<li>Spike-and-slab</li>
</ul>
<p>Probability Distributions:</p>
<ul>
<li>Normal</li>
<li>Gamma</li>
<li>Poisson</li>
<li>Dirichlet</li>
<li>Beta</li>
<li>Bernoulli</li>
<li>Binomial</li>
<li>Cauchy</li>
<li>Student's t</li>
<li>Snedecor's F</li>
<li>Power law</li>
<li>Log normal</li>
<li>Multinomial</li>
<li>Inverse Wishart</li>
</ul>
<p>Hyperparameters:</p>
<ul>
<li>Bayesian Optimization hyperparameter tuning methods</li>
<li>Sequential Model Based Optimization hyperparameter tuning methods</li>
</ul>
<p>Hypothesis Testing:</p>
<ul>
<li>t-Test</li>
<li>Wilcoxon test</li>
</ul>
<p>Miscallaneous:</p>
<ul>
<li>Conditional random fields</li>
<li>Support vector machines</li>
<li>Maximum entropy</li>
<li>Neural networks</li>
<li>Ising models</li>
<li>Boltzmann machines</li>
<li>Restricted Boltzmann machines</li>
<li>Perceptrons</li>
<li>Boosting</li>
<li>Discriminative models vs. generative models</li>
<li>IID model</li>
<li>Shannon information</li>
<li>f-Divergence</li>
<li>Entropy</li>
<li>KL-Divergence</li>
<li>Mixture model</li>
<li>Hidden Markov model</li>
<li>Latent Dirichlet allocation</li>
<li>Mixed membership model</li>
<li>Hierarchical model</li>
<li>Alternating maximization</li>
<li>EM Algorithm</li>
<li>Semi-supervised learning</li>
<li>Reinforcement learning</li>
<li>Bandit algorithms</li>
<li>PAC bounds</li>
<li>Bayesian network</li>
<li>Sentiment analysis</li>
<li>Text regression
Backpropagation
Case-based reasoning
Rule-based reasoning
Gaussian process
Spline
Loess
Density estimation
KDE
Histogram
Vector quantization
MDL
Bagging
Bootstrap
Jackknife
Fisher's linear discriminant analysis
Basis regression
Polynomial regression</li>
</ul>
<p>Reinforcement learning:</p>
<ul>
<li>Temporal difference learning</li>
<li>Q-learning</li>
<li>SARSA learning</li>
</ul>
<p>Association rule learning</p>
<ul>
<li>Apriori algorithm</li>
</ul>
<p>Clustering:</p>
<ul>
<li>k-Means</li>
<li>dp-Means</li>
<li>Hierarchical clustering</li>
<li>Single-linkage clustering</li>
</ul>
</body>
</html>