-
Notifications
You must be signed in to change notification settings - Fork 0
/
Course Sched Machine Learning.htm
322 lines (277 loc) · 10.1 KB
/
Course Sched Machine Learning.htm
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
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
<title>Machine Learning</title>
<meta http-equiv="content-type" content="text/html; charset=utf-8" />
<link rel="stylesheet" href="./../assets/berry/css/main.css">
<link rel="stylesheet" href="./../assets/berry/css/course-cs229.css">
<script type="text/javascript" src="./../assets/berry/js/jquery.js"></script>
<script type="text/javascript" src="./../assets/berry/js/mathjax/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
</head>
<body>
<div id="container">
<div id="header" onclick="window.location='http://www.ml-class.org/course/class/index'">
<div id="header_links">
<a href="http://www.ml-class.org/course/auth/reportbug?info=4359fee80c0a8cfe51cf16cdb31ff9808a051d4a%7CnY7EE55AOgjVckmefPRleZqTwoAQPvkHaNyVQXthjIPIIujTNLPK%2Bwo1dDkBy0GPjkp6o459%2Fu%2B3KpDF6lfpzZEAMrLrxAwLVMIKn4OzAtNMFMN%2BA3Wlt0rtF2Ly0zz9NhIzPWQZDH5Dw1jsgdn5UQDl7mDsvYMWV1%2FirVv4jy0w697D%2BobBe2bQ6LbUvJxz0LsxL%2FP5XhUdLiFc20h8RHhmhRIJAWKsiWtHfdvs944dwKlX7%2BfZBAxcamBvvOCrghQPDSp62vJitQplDw%2F2mv3bs2Wu4sjN9MMvgv7enoo01vIdjiSHfu0q7XJG8sXU0IKIVtlILCxxGHCYfvjUYQKzMMrp6%2FDUNrz9SEm4yAkkPM55NOCBTtNGCnJFG9E%2Bkgef4W9kz5uW8%2Fnu7YoE1gJVnOaygumt7ibEIKPdN1g%3D">Feedback</a> <a href="http://www.ml-class.org/course/class/preferences">Preferences</a>
<a href="http://www.ml-class.org/course/auth/logout">Logout</a> </div>
</div>
<div id="content_body">
<div id="navbar">
<p class="navbar_item navbar_person">Bryan Wolfford</p>
<p class="navbar_item navbar_home"><a href="http://www.ml-class.org/course/class/index">Home</a></p>
<p class="navbar_item navbar_progress"><a href="http://www.ml-class.org/course/class/progress">Course Progress</a></p> <p class="navbar_item navbar_video"><a href="http://www.ml-class.org/course/video/list">Video Lectures</a></p>
<p class="navbar_item navbar_homeworks"><a href="http://www.ml-class.org/course/homework/list">Programming Exercises</a></p><p class="navbar_item navbar_quizzes"><a href="http://www.ml-class.org/course/quiz/list?type=quiz">Review Questions</a></p>
<p class="navbar_item navbar_qna"><a href="http://www.ml-class.org/course/qna/index">Q&A Forum</a></p>
<p class="navbar_item navbar_resources"><a href="/course/resources/index?page=course-schedule">Course Schedule</a></p><p class="navbar_item navbar_resources"><a href="/course/resources/index?page=course-materials">Course Materials</a></p><p class="navbar_item navbar_resources"><a href="/course/resources/index?page=course-info">Course Information</a></p><p class="navbar_item navbar_resources"><a href="/course/resources/index?page=octave-install">Octave Installation</a></p> <p class="navbar_item navbar_aboutus"><a href="http://www.ml-class.org/course/aboutus/index">About Us</a></p>
</div>
<div id="content">
<style type="text/css">
.section_container {
clear: both;
padding: 5px 15px 15px 15px;
border: 1px solid #999;
border-top-right-radius: 5px;
border-bottom-left-radius: 5px;
border-bottom-right-radius: 5px;
overflow: hidden;
box-shadow: 0px 0px 10px #ccc;
margin-bottom: 13px;
}
.section_item {
border-bottom: 1px solid #999;
padding: 10px 0px;
overflow: hidden;
vertical-align: middle;
}
.section_text {
font-weight: bold;
font-size: 125%;
display: inline;
margin-left: 5px;
margin-top: 15px;
float: left;
}
.section_download {
display: inline;
padding-left: 10px;
float:right;
}
.section_date {
font-weight: bold;
font-size:115%;
}
.section_lectures {
display: inline;
float: left;
width: 300px;
}
.section_duedates {
display: inline;
float: right;
width: 300px;
}
</style>
<div id="content">
<h1>Course Schedule</h1>
<div class="section_container">
<div class="section_item">
<div class="section_date">
10th - 16th October
</div>
<div class="section_lectures">
<ul>
<li>Introduction</li>
<li>Linear regression with one variable</li>
<li>(Optional) Linear algebra review</li>
</ul>
</div>
<div class="section_duedates">
<b>Due 16 Oct 23:59:59 PDT</b>
<ul>
<li>Review Questions (for the week's topics)</li>
</ul>
</div>
</div>
<div class="section_item">
<div class="section_date">
17th - 23rd October
</div>
<div class="section_lectures">
<ul>
<li>Linear regression with multiple variables</li>
<li>Octave tutorial</li>
</ul>
</div>
<div class="section_duedates">
<b>Due 23 Oct 23:59:59 PDT</b>
<ul>
<li>Review Questions (for the week's topics)</li>
<li>Programming Exercise 1 <br /> (Linear regression)</li>
</ul>
</div>
</div>
<div class="section_item">
<div class="section_date">
24th - 30th October
</div>
<div class="section_lectures">
<ul>
<li>Logistic Regression</li>
<li>One-vs-all Classification</li>
<li>Regularization</li>
</ul>
</div>
<div class="section_duedates">
<b>Due 30 Oct 23:59:59 PDT</b>
<ul>
<li>Review Questions (for the week's topics)</li>
<li>Programming Exercise 2 <br /> (Logistic regression)</li>
</ul>
</div>
</div>
<div class="section_item">
<div class="section_date">
31st October - 6th November
</div>
<div class="section_lectures">
<ul>
<li>Neural Networks</li>
<li>Backpropagation Algorithm</li>
</ul>
</div>
<div class="section_duedates">
<b>Due 6 Nov 23:59:59 PDT</b>
<ul>
<li>Review Questions (for the week's topics)</li>
<li>Programming Exercise 3 <br /> (Neural Networks)</li>
</ul>
</div>
</div>
<div class="section_item">
<div class="section_date">
7th - 13th November
</div>
<div class="section_lectures">
<ul>
<li>Support Vector Machines (SVMs)</li>
<li>Survey of other algorithms: Naive Bayes, Decision Trees, Boosting</li>
</ul>
</div>
<div class="section_duedates">
<b>Due 13 Nov 23:59:59 PDT</b>
<ul>
<li>Review Questions (for the week's topics)</li>
<li>Programming Exercise <br /> (Support Vector Machines)</li>
</ul>
</div>
</div>
<div class="section_item">
<div class="section_date">
14th - 20th November
</div>
<div class="section_lectures">
<ul>
<li>Practical advise for applying learning algorithms</li>
<li>How to develop and debug learning algorithms</li>
<li>Feature and model design, setting up experiments</li>
</ul>
</div>
<div class="section_duedates">
<b>Due 20 Nov 23:59:59 PDT</b>
<ul>
<li>Review Questions (for the week's topics)</li>
<li>Programming Exercise (TBA)</li>
</ul>
</div>
</div>
<div class="section_item">
<div class="section_date">
21st - 27th November
</div>
<div class="section_lectures">
<ul>
<li>Unsupervised learning: Agglomerative clustering, k-Means, PCA</li>
<li>(Optional) Idependent component analysis</li>
</ul>
</div>
<div class="section_duedates">
<b>Due 27 Nov 23:59:59 PDT</b>
<ul>
<li>Review Questions (for the week's topics)</li>
<li>Programming Exercise (TBA)</li>
</ul>
</div>
</div>
<div class="section_item">
<div class="section_date">
28th November - 4th December
</div>
<div class="section_lectures">
<ul>
<li>Anomaly detection</li>
<li>Combining unsupervised and supervised learning.</li>
</ul>
</div>
<div class="section_duedates">
<b>Due 4 Dec 23:59:59 PDT</b>
<ul>
<li>Review Questions (for the week's topics)</li>
<li>Programming Exercise (TBA)</li>
</ul>
</div>
</div>
<div class="section_item">
<div class="section_date">
5th - 11th December
</div>
<div class="section_lectures">
<ul>
<li>Other applications: Recommender systems. Learning to rank</li>
</ul>
</div>
<div class="section_duedates">
<b>Due 11 Dec 23:59:59 PDT</b>
<ul>
<li>Review Questions (for the week's topics)</li>
<li>Programming Exercise (TBA)</li>
</ul>
</div>
</div>
<div class="section_item">
<div class="section_date">
12th - 16th December
</div>
<div class="section_lectures">
<ul>
<li>Large-scale/parallel machine learning and big data.</li>
<li>Machine learning design / practical methods</li>
<li>Team design of machine learning systems</li>
</ul>
</div>
<div class="section_duedates">
<b>Due 16 Dec 23:59:59 PDT</b>
<ul>
<li>Review Questions (for the week's topics)</li>
<li>Programming Exercise (TBA)</li>
</ul>
</div>
</div>
</div>
</div>
<br />
<br />
</div> <!-- content -->
</div> <!-- content-body -->
</div> <!-- container -->
<script type="text/javascript">
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-4245877-6']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
</script>
</body>
</html>