-
Notifications
You must be signed in to change notification settings - Fork 0
/
cube.html
105 lines (104 loc) · 4.05 KB
/
cube.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
---
layout: page
title: The Cube
weight: 0
---
<section class="main-container text">
<div class="main">
<p> The entries on this page will be gradually filled in as the course progresses.</p>
<table class="table">
<h3>Supervised Learning</h3>
<br>
<tr>
<td class="col-xs-2"></td>
<th scope="col" class="col-xs-5">Discrete</th>
<th scope="col" class="col-xs-5">Continuous</th>
</tr>
<tr>
<th scope="row">Probabilistic</th>
<td>
<ul>
<!--
<li><a href="{{ site.baseurl }}/recaps/lec5" target="_blank">Lecture 5 - Probabilistic Classification</a></li>
-->
</ul>
</td>
<td>
<ul>
<!--
<li><a href="{{ site.baseurl }}/recaps/lec3" target="_blank">Lecture 3 - Probabilistic Regression</a></li>
-->
</ul>
</td>
</tr>
<tr>
<th scope="row">Nonprobabilistic</th>
<td>
<ul>
<!--
<li><a href="{{ site.baseurl }}/recaps/lec1" target="_blank">Lecture 1 - KNN Classification</a></li>
<li><a href="{{ site.baseurl }}/recaps/lec4" target="_blank">Lecture 4 - Linear Classification</a></li>
<li><a href="{{ site.baseurl }}/recaps/lec8" target="_blank">Lecture 8 - Neural Networks 1</a></li>
<li><a href="{{ site.baseurl }}/recaps/lec9" target="_blank">Lecture 9 - Neural Networks 2</a></li>
<li><a href="{{ site.baseurl }}/recaps/lec10" target="_blank">Lecture 10 - Support Vector Machines 1</li>
<li><a href="{{ site.baseurl }}/recaps/lec11" target="_blank">Lecture 11 - Support Vector Machines 2</li>
-->
</ul>
</td>
<td>
<ul>
<!--
<li><a href="{{ site.baseurl }}/recaps/lec1" target="_blank">Lecture 1 - KNN and Kernel Regression</a></li>
<li><a href="{{ site.baseurl }}/recaps/lec2" target="_blank">Lecture 2 - Linear Regression</a></li>
<li><a href="{{ site.baseurl }}/recaps/lec8" target="_blank">Lecture 8 - Neural Networks 1</a></li>
<li><a href="{{ site.baseurl }}/recaps/lec9" target="_blank">Lecture 9 - Neural Networks 2</a></li>
-->
</ul>
</td>
</tr>
</table>
<table class="table">
<h3>Unsupervised Learning</h3>
<br>
<tr>
<td class="col-xs-2"></td>
<th scope="col" class="col-xs-5">Discrete</th>
<th scope="col" class="col-xs-5">Continuous</th>
</tr>
<tr>
<th scope="row">Probabilistic</th>
<td>
<ul>
<!--
<li><a href="{{ site.baseurl }}/recaps/lec14" target="_blank">Lecture 14 - Mixture Models</a></li>
-->
</ul>
</td>
<td>
<ul>
<!--
<li><a href="{{ site.baseurl }}/recaps/lec16" target="_blank">Lecture 16 - Topic Models</a></li>
-->
</ul>
</td>
</tr>
<tr>
<th scope="row">Nonprobabilistic</th>
<td>
<ul>
<!--
<li><a href="{{ site.baseurl }}/recaps/lec13" target="_blank">Lecture 13 - HAC and K-Means</a></li>
-->
</ul>
</td>
<td>
<ul>
<!--
<li><a href="{{ site.baseurl }}/recaps/lec15" target="_blank">Lecture 15 - Principal Component Analysis</a></li>
-->
</ul>
</td>
</tr>
</table>
</div>
</section>