-
Notifications
You must be signed in to change notification settings - Fork 2
/
students.html
324 lines (285 loc) · 15.8 KB
/
students.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
<!DOCTYPE html>
<html prefix="og: http://ogp.me/ns#" lang="en">
<head>
<title>Courses</title>
<link rel="stylesheet" type="text/css" href="/styles/styles.css">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta charset="utf-8">
<link rel="apple-touch-icon" sizes="180x180" href="/assets/apple-touch-icon.png">
<link rel="icon" type="image/png" href="/assets/favicon.ico">
<link rel="manifest" href="/assets/site.webmanifest">
<link rel="shortcut icon" href="/assets/favicon.ico">
<meta name="msapplication-TileColor" content="#da532c">
<meta name="msapplication-config" content="/assets/browserconfig.xml">
<meta name="theme-color" content="#ffffff">
<meta property="og:title" content="Courses"/>
<meta property="og:type" content="website"/>
<meta property="og:image" content="/assets/logotype_me.png"/>
<meta name="twitter:card" content="summary_large_image">
<style>h1 {text-align: center;}
h2 {text-align: center;}
.inp{
width: 300px;
}
</style>
</head>
<body>
<!--<span id = "spn">Dark Mode</span>-->
<header id="header">
<a href="/index.html"><img src="/assets/iit.png" width="65" height="65" class="center"></a>
</header>
<br><br><br><br>
<div id="container" style="color: white;">
<nav>
<ul>
<li><a>Home</a>
<!--Fridt Tier Drop Down-->
<ul>
<li><a href="/index.html">Home</a></li>
</ul>
<li><a>Curriculum</a>
<!--Fridt Tier Drop Down-->
<ul>
<li><a href="/course.html">Courses</a></li>
<li><a href="/course_btech.html">B.Tech</a></li>
<li><a href="/course_mtech.html">M.Tech</a></li>
</ul>
<li><a>Research Areas</a>
<!--Fridt Tier Drop Down-->
<ul>
<li><a href="/research_areas.html">Research Areas</a></li>
</ul>
<li><a>Projects</a>
<!--Fridt Tier Drop Down-->
<ul>
<li><a href="/projects.html">Projects</a></li>
</ul>
<li><a>People</a>
<!--Fridt Tier Drop Down-->
<ul>
<li><a href="/course_btech.html">Faculty</a></li>
<li><a href="/course_mtech.html">Students</a></li>
</ul>
<li><a>Contact</a>
<!--Fridt Tier Drop Down-->
<ul>
<li><a>Contact</a></li>
</ul>
<li><a>About</a>
<!--Fridt Tier Drop Down-->
<ul>
<li><a href="/about.html">About</a></li>
</ul>
</ul>
</nav>
</div>
<br>
<br>
<br>
<div>
</div>
<div id="spn">
<h1>Course Page </h1>
<h2>Data Science & Artificial Intelligence</h2>
<hr>
<br><br><br>
<style>
</style>
<p >
<br>
This page holds all the courses that the department offers. Course contents are updated and maintained periodically.
Students are advised to keep checking for any important updates.
<br><br><br>
<div class="searchforcourse" style="text-align: center;">
<input type="text" class="inp" id="myInput" onkeyup="myFunction()" placeholder="Search for Courses" title="Type in a name">
<ul class="ul" id="myUL">
<li><a href="/Course_Pages/DS100.html">DS 100: Mathematical Foundations of Data Science (4 Credits)</a></li>
<li><a href="/Course_Pages/DS200.html">DS 200: Architecture for Management of Large Datasets (6 Credits)</a></li>
<li><a href="/Course_Pages/DS201.html">DS 201: Statistical Programming (4 Credits)</a></li>
<li><a href="/Course_Pages/DS250.html">DS 250: Data Analytics and Visualization (6 Credits)</a></li>
<li><a href="/Course_Pages/DS251.html">DS 251: Artificial Intelligence (6 Credits)</a></li>
<li><a href="/Course_Pages/DS252.html">DS 252: DSAI Lab (2 Credits)</a></li>
<li><a href="/Course_Pages/DS500.html">DS 500: Big Data Algorithms (6 Credits)</a></li>
<li><a href="/Course_Pages/DS501.html">DS 501: Information Retrieval (6 Credits)</a></li>
<li><a href="/Course_Pages/DS503.html">DS 503: Advanced Data Analytics (6 Credits)</a></li>
<li><a href="/Course_Pages/DS601.html">DS 601: Digital Image Processing (6 Credits)</a></li>
</ul>
</div>
<br>
<br>
<br>
The following are the list of courses:
<br><br><br>
<li><a target="_blank" rel="noopener" href="/Course_Pages/DS100.html">DS 100: Mathematical Foundations of Data Science (4 Credits)</a>
</li>
<br>
<p>Bayes Rule and its connection to inference, various sampling methods, Modern PAC analysis
(probably approximately correct).
Geometry of high-dimensional space, distance metrics used for numerical and text data.
Locality sensitive hashing (LSH).
Matrix approximation techniques: Principal Component Analysis, SVD and dimensionality
reduction.
Application of transforms (Fourier, Laplace) to data analysis.
Linear regression problem, gradient descent.
Introduce some representative datasets using images, documents and tables. Use
Matlab/Python/R to demonstrate and explore basic concepts.<br>
Prerequisites: NA</p><hr><br>
<li>
<a target="_blank" rel="noopener" href="/Course_Pages/DS200.html">DS 200: Architecture for Management of Large Datasets (6 Credits)</a>
</li><br>
<p>Design of distributed program models and abstractions, such as MapReduce, Dataflow and
Vertex-centric models, for processing volume, velocity, and linked datasets, and for storing
and querying over NoSQL datasets.
Approaches and design patterns to translate existing data-intensive algorithms and analytics
into these distributed programming abstractions.
Distributed software architectures, runtime and storage strategies used by Big Data platforms
such as Apache Hadoop, Spark, Storm, Giraph, and Hive to execute applications developed
using these models on commodity clusters and Clouds in a scalable manner. Design of
distributed program models and abstractions, such as Map Reduce, Dataflow and Vertexcentric models, for processing volume, velocity, and linked datasets, and for storing and
querying over NoSQL datasets.
Approaches and design patterns to translate existing data-intensive algorithms and analytics
into these distributed programming abstractions.
Distributed software architectures, runtime and storage strategies used by Big Data platforms
such as Apache Hadoop, Spark, Storm, Giraph and Hive to execute applications developed
using these models on commodity clusters and Clouds in a scalable manner.<br>
Prerequisites: NA</p><hr>
<br>
<li>
<a target="_blank" rel="noopener" href="/Course_Pages/DS201.html">DS 201: Statistical Programming (4 Credits)</a>
</li><br>
<p>Probability and statistics: Review, Statistical measures and tests, Statistical analyses using
Rand Python, and MATLAB, Linear Regression, Hypothesis Testing, Resampling Techniques,
and Bootstrapping, Introduction to contemporary statistical packages<br>
Prerequisites: NA</p><hr>
<br>
<li>
<a target="_blank" rel="noopener" href="/Course_Pages/DS250.html">DS 250: Data Analytics and Visualization (6 Credits)</a>
</li><br>
<p>Data science workflow, Automated methods for data collection, Data and Visualization
Models, Data wrangling and cleaning, Exploratory data analysis
Building Models for: Classification, Clustering, Regression, Time-series, Association Analysis,
Recommendation Systems.
Model evaluation, statistical tests for significance of predictors. Model regularization: ridge,
lasso, elastic-net.
Visualization Software and Tools, Visualization Design, Multidimensional Data, Graphical
Perception, Interaction dynamics for Visual Analysis, Using Space Effectively, Stacked
Graphs, Geometry & Aesthetics.
Networks, Graph Visualization and navigation in information Visualization, mapping &
Cartography, Text Visualization. <br>
Prerequisites: NA</p><hr>
<br>
<li>
<a target="_blank" rel="noopener" href="/Course_Pages/DS251.html">DS 251: Artificial Intelligence (6 Credits)
</a>
</li><br>
<p>Problem solving, search techniques, control strategies, game playing (mini-max), reasoning,
knowledge representation through predicate logic, rule-based systems, semantic nets,
frames, conceptual dependency formalism. Planning. Handling uncertainty: Bayesian
Networks, Dempster-Shafer theory, certainty factors, Fuzzy logic, Learning through Neural
nets - Backpropagation, radial basis functions, Neural computational models - Hopfield Nets,
Bolzman machines, MATLAB programming, introduction to Machine Learning, Supervised
and Unsupervised Learning, Introduction to Machine Learning libraries. <br>
Prerequisites: NA</p><hr>
<br>
<li>
<a target="_blank" rel="noopener" href="/Course_Pages/DS252.html">DS 252: DSAI Lab (2 Credits)</a>
</li><br>
<p>Introduction, Data in Data Analytics, Descriptive Statistics, Programming with R, Probability
Distributions, Sampling Distributions, Statistical Inference, Statistical Tables Relation
Analysis, Analysis of Variance (ANOVA), Bayesian Classifier, Information Based
Classification.
Support Vector Machine Sensitivity Analysis Similarity Measures.<br>
Prerequisites: NA</p><hr>
<br>
<li>
<a target="_blank" rel="noopener" href="/Course_Pages/DS500.html">DS 500: Big Data Algorithms (6 Credits)
</a>
</li><br>
<p>Introduction to big data and its peculiarities. Map Reduce as a datacenter-scale programming
abstraction. Parallel algorithm design to process massive datasets. Algorithms to solve
problems from a variety of domains: web search, e-commerce, social-networking, machine
learning. Streaming Algorithms, sketching algorithms. Brief discussion of next generation
systems like Spark and Flink.<br>
Prerequisites: Introductory courses in probability, statistics, linear algebra and algorithms.</p><hr>
<br>
<li>
<a target="_blank" rel="noopener" href="/Course_Pages/DS501.html">DS 501: Information Retrieval (6 Credits)</a>
</li><br>
<p>Introduction, Document Indexing, Storage and Compression, Retrieval Models, Performance
Evaluation, Text Categorization and Filtering, Text Clustering, Web Information Retrieval,
Learning to rank, Advanced Topics (Text Summarization, Question answering, Recommender
Systems)<br>
Prerequisites: NA</p><hr>
<br>
<li>
<a target="_blank" rel="noopener" href="/Course_Pages/DS503.html">DS 503: Advanced Data Analytics (6 Credits)</a>
</li><br>
<p>Analysis techniques for high dimensional datasets; Algorithms for massive data problems;
Graph representation learning and Graph Neural Networks; Link Prediction, Graph and Node
classification, Applications of Graph learning; Network algorithms including those for the World
Wide Web; Clustering algorithms for high dimensional datasets; Advanced techniques for
Time Series analysis: Motifs, Anomaly detection, Matrix Profile Technique<br>
Prerequisites: DS250 or equivalent.</p><hr>
<br>
<li>
<a target="_blank" rel="noopener" href="/Course_Pages/DS601.html">DS 601: Digital Image Processing (6 Credits)</a>
</li><br>
<p>Fundamentals - Visual perception, image sampling and quantization; Intensity transformations
- nonlinear transformations for enhancement, histogram equalization; Spatial filtering -
convolution, linear and order statistic filters, unsharp masking. Image Transforms - discrete
Fourier transform, discrete cosine transform; Transform domain processing - image
smoothing, specialized filters (Gaussian, Laplacian, etc); Image restoration - using spatial
filters, Wiener filter; Introduction to colour spaces and colour image processing; orphological
image processing - erosion and dilation, opening and closing, hit-or-miss transform, thinning
and shape decomposition; Binarization and Image segmentation - edge detection,
thresholding, region-based segmentation; Image compression - fundamentals, lossless
coding, predictive coding, transform coding.<br>
Prerequisites: NA</p><hr>
</p>
<br>
<!--Also, if that thing in the URL is annoying, just click on it to pause it.-->
<br>
<br>
<p style="text-align: center;"><a href="/course.html"> Curriculum</a> / <a href="/research_areas.html">Research Areas</a> / <a href="/projects.html">Projects</a> / <a href="/about.html">About</a></p>
<br>
<p style="text-align:center;"><a href="https://github.com/HS1VT"><img src="/assets/git.png" height="50" width = "50"></a>
<br> Copyright © Vishesh Thakur
</p>
</div>
</div>
<script type="text/javascript" src="/scripts/uni.js"></script>
<script>
myFunction()
if (document.readyState !== 'loading'){
uni();
} else {
document.addEventListener('DOMContentLoaded', uni);
}
document.getElementById("myUL").display="block";
function myFunction() {
if (!document.getElementById("myInput").value){
document.getElementById("myUL").style.display="none";
}
else{
document.getElementById("myUL").style.display="block";
}
var input, filter, ul, li, a, i, txtValue;
input = document.getElementById("myInput");
filter = input.value.toUpperCase();
ul = document.getElementById("myUL");
li = ul.getElementsByTagName("li");
for (i = 0; i < li.length; i++) {
a = li[i].getElementsByTagName("a")[0];
txtValue = a.textContent || a.innerText;
if (txtValue.toUpperCase().indexOf(filter) > -1) {
li[i].style.display = "";
} else {
li[i].style.display = "none";
}
}
}
</script>
<script type="text/javascript" src="/scripts/darkMode.js"></script>
<script src="//instant.page/1.1.0" type="module" integrity="sha384-EwBObn5QAxP8f09iemwAJljc+sU+eUXeL9vSBw1eNmVarwhKk2F9vBEpaN9rsrtp"></script>
</body>
</html>