Fall 2023: Open Source Software (BA Prog) - Revision history

Mkwiki: /* Resources */

2023-08-27T14:26:29Z

Resources

Mkwiki: /* Course Books: */

2023-08-27T14:26:13Z

Course Books:

Mkwiki: /* Lectures */

2023-08-27T14:17:46Z

Lectures

Mkwiki: /* Lectures */

2023-08-27T14:12:15Z

Lectures

Mkwiki: /* Course Overview */

2023-08-27T13:41:44Z

Course Overview

Mkwiki at 13:40, 27 August 2023

2023-08-27T13:40:23Z

Mkwiki at 13:39, 27 August 2023

2023-08-27T13:39:48Z

Mkwiki: Created page with "Open Source Software"

2023-08-13T11:13:21Z

Created page with "Open Source Software"

New page

Open Source Software

← Older revision		Revision as of 14:26, 27 August 2023
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	* '''R4''': A Beginner's Guide to Gambas by John W. Rittinghouse <br>		* '''R4''': A Beginner's Guide to Gambas by John W. Rittinghouse <br>
	* '''R5''': The Book of Gimp, Lecarme, O. and Delvare, K., No Starch press, 2013.		* '''R5''': The Book of Gimp, Lecarme, O. and Delvare, K., No Starch press, 2013.
−
−	~~===References:===~~
−	* '''R2''': Data Mining: Concepts and Techniques, 3nd edition,Jiawei Han and Micheline Kamber. <br>
−	* '''R3''': Data Mining: A Tutorial Based Primer, Richard Roiger, Michael Geatz, Pearson Education 2003. <br>
−	* '''R4''': Introduction to Data Mining with Case Studies, G.K. Gupta, PHI 2006. <br>
−	* '''R5''': Insight into Data mining: Theory and Practice, Soman K. P., DiwakarShyam, Ajay V., PHI 2006

@@ Line 59: / Line 59: @@
 == Resources ==
 ===Course Books:===
-* '''CB1''': Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson Education. <br>
+* '''R1''': FUNDAMENTALS OF OPEN SOURCE SOFTWARE By M. N. RAO PHI Publication <br>
 ===References:===
 * '''R2''': Data Mining: Concepts and Techniques, 3nd edition,Jiawei Han and Micheline Kamber. <br>

@@ Line 19: / Line 19: @@
 | style="width: 60%" |  '''''Introduction to Open Source Softwares'''''
 | style="width: 15%" |
-| Chapter 1 (CB1)
+| Chapter xx
 |-
 | Unit 2
-|   '''''Data mining techniques''''': 2.1- Types of data, 2.2 – Data Quality, 2.3.1 Aggregation, 2.3.2 Sampling, 2.3.3 Dimensionality reduction – upto pg 51, 2.3.4 Feature subset selection upto pg 52, 2.4.5 Feature creation upto pg 55, 2.3.6 Discretization upto pg 59, 2.3.7 variable transformations 2.4.3 Dissimilarity among data objects 2.4.4 similarity among data objects
+|   '''''Linux Operating System'''''
-|  [http://mkbhandari.com/mkwiki/data/spring2023/DM/2DMT.pdf '''2DMT.pdf''']
-| Chapter 2 (CB1)
+| Chapter xx
 |-
-| Unit 5 / Chapter 6
+| Unit 3
-|   '''''Association Rules''''': 6.1-Problem definition, 6.2-Frequent itemset generation, 6.3-Rule generation till Pg 351
+|   '''''Understanding Open Source and Free Software Licensing'''''
-|  [http://mkbhandari.com/mkwiki/data/spring2023/DM/3AR.pdf '''3AR.pdf''']
-| Chapter 6 (CB1)
+| Chapter xx
 |-
-| Unit 3 / Chapter 4
+| Unit 4
-|   '''''Classification: Basic Concepts and Techniques''''': 4.1 – Preliminaries, 4.2 – General Approach to Solving a Classification Problem, 4.3 Decision Tree Induction (Till Pg. 165), 4.5 –Evaluating the Performance of a Classifier
+|   '''''Study of commercial application software vs OSS'''''
-|  [http://mkbhandari.com/mkwiki/data/spring2023/DM/4CL.pdf '''4Classification.pdf''']
-| Chapter 4 (CB1)
+| Chapter xx
 |-
-| Unit 4 / Chapter 5
+| Unit 5
-|   '''''Classification: Alternative Techniques''''': 5.1 – Rule Based Classifier (upto page 212),5.2 – Nearest Neighbor Classifiers, 5.3–Bayesian Classifiers (Complete for discrete data and only introduction of Bayes classifier for continuous attributes) till pg. 233, 5.7.1 –Alternative Metrics
+|   '''''Introduction to Gambus'''''
-|  [https://www-users.cse.umn.edu/~kumar001/dmbook/index.php#item4 Read from Authors' web page]
-| Chapter 5 (CB1)
+| Chapter xx
 |-
-| Unit 6 / Chapter 8
+| Unit 6
-|   '''''Clustering''''': 8.1 Basic concepts of clustering analysis, 8.2 K-Means (8.2.1-8.2.5 except 8.2.3), 8.3 Agglomerative Hierarchical Clustering (except pg 522-524), 8.4 DBSCAN
+|   '''''Introduction to GIMP'''''
-|  [http://mkbhandari.com/mkwiki/data/spring2023/DM/5CA.pdf '''5CA.pdf''']
-| Chapter 8 (CB1)
+| Chapter xx
 |}

@@ Line 16: / Line 16: @@
 !Readings
 |-
-| style="width: 12%; " | Unit 1 / Chapter 1
+| style="width: 12%; " | Unit 1
-| style="width: 60%" |  '''''Introduction''''': 1.1 - What Is Data Mining? 1.2 Challenges 1.3 Data Mining Origins 1.4 Data Mining Tasks
+| style="width: 60%" |  '''''Introduction to Open Source Softwares'''''
-| style="width: 15%" | [http://mkbhandari.com/mkwiki/data/spring2023/DM/1Intro.pdf '''1Intro.pdf''']
+| style="width: 15%" |
 | Chapter 1 (CB1)
 |-
-| Unit 2 / Chapter 2
+| Unit 2
 |   '''''Data mining techniques''''': 2.1- Types of data, 2.2 – Data Quality, 2.3.1 Aggregation, 2.3.2 Sampling, 2.3.3 Dimensionality reduction – upto pg 51, 2.3.4 Feature subset selection upto pg 52, 2.4.5 Feature creation upto pg 55, 2.3.6 Discretization upto pg 59, 2.3.7 variable transformations 2.4.3 Dissimilarity among data objects 2.4.4 similarity among data objects
 |  [http://mkbhandari.com/mkwiki/data/spring2023/DM/2DMT.pdf '''2DMT.pdf''']

@@ Line 6: / Line 6: @@
 == Course Overview ==
-* As per the Delhi University [https://cs.du.ac.in/uploads/ug_guidelines/BSc-H-CS/VI/DM%20Guidelines%2014Jan2022.pdf Course Guidelines]
+* As per the Delhi University [https://cs.du.ac.in/uploads/ug_guidelines/BA/BACS09A-oss%20guidelineslocf.pdf Course Guidelines]
 == Lectures ==

@@ Line 1: / Line 1: @@
 == Logistics ==
-*Class Timings: '''Mondays''' and  '''Thursdays''' 8:30 am - 9:30 am (1<sup>st</sup> slot)
+*Class Timings: '''Mondays''' and  '''Tuesdays''' 8:30 am - 9:30 am (1<sup>st</sup> slot)
 *Classroom: R45
 *Lab Timings: '''Fridays''' 8:30 am - 12:30 pm  (1<sup>st</sup> - 4<sup>th</sup>slots)

← Older revision		Revision as of 13:39, 27 August 2023
Line 1:		Line 1:
−	~~Open Source Software~~	+	== Logistics ==
		+	*Class Timings: '''Mondays''' and '''Thursdays''' 8:30 am - 9:30 am (1<sup>st</sup> slot)
		+	*Classroom: R45
		+	*Lab Timings: '''Fridays''' 8:30 am - 12:30 pm (1<sup>st</sup> - 4<sup>th</sup>slots)
		+	*Labs: CL-3
		+
		+	== Course Overview ==
		+	* As per the Delhi University [https://cs.du.ac.in/uploads/ug_guidelines/BSc-H-CS/VI/DM%20Guidelines%2014Jan2022.pdf Course Guidelines]
		+
		+	== Lectures ==
		+	{\| class="wikitable" style="text-align: left; width: 100%";
		+	\|-
		+	!Lecture
		+	!Topic
		+	!Lecture Slides
		+	!Readings
		+	\|-
		+	\| style="width: 12%; " \| Unit 1 / Chapter 1
		+	\| style="width: 60%" \| '''''Introduction''''': 1.1 - What Is Data Mining? 1.2 Challenges 1.3 Data Mining Origins 1.4 Data Mining Tasks
		+	\| style="width: 15%" \| [http://mkbhandari.com/mkwiki/data/spring2023/DM/1Intro.pdf '''1Intro.pdf''']
		+	\| Chapter 1 (CB1)
		+	\|-
		+	\| Unit 2 / Chapter 2
		+	\| '''''Data mining techniques''''': 2.1- Types of data, 2.2 – Data Quality, 2.3.1 Aggregation, 2.3.2 Sampling, 2.3.3 Dimensionality reduction – upto pg 51, 2.3.4 Feature subset selection upto pg 52, 2.4.5 Feature creation upto pg 55, 2.3.6 Discretization upto pg 59, 2.3.7 variable transformations 2.4.3 Dissimilarity among data objects 2.4.4 similarity among data objects
		+	\| [http://mkbhandari.com/mkwiki/data/spring2023/DM/2DMT.pdf '''2DMT.pdf''']
		+	\| Chapter 2 (CB1)
		+	\|-
		+	\| Unit 5 / Chapter 6
		+	\| '''''Association Rules''''': 6.1-Problem definition, 6.2-Frequent itemset generation, 6.3-Rule generation till Pg 351
		+	\| [http://mkbhandari.com/mkwiki/data/spring2023/DM/3AR.pdf '''3AR.pdf''']
		+	\| Chapter 6 (CB1)
		+	\|-
		+	\| Unit 3 / Chapter 4
		+	\| '''''Classification: Basic Concepts and Techniques''''': 4.1 – Preliminaries, 4.2 – General Approach to Solving a Classification Problem, 4.3 Decision Tree Induction (Till Pg. 165), 4.5 –Evaluating the Performance of a Classifier
		+	\| [http://mkbhandari.com/mkwiki/data/spring2023/DM/4CL.pdf '''4Classification.pdf''']
		+	\| Chapter 4 (CB1)
		+	\|-
		+	\| Unit 4 / Chapter 5
		+	\| '''''Classification: Alternative Techniques''''': 5.1 – Rule Based Classifier (upto page 212),5.2 – Nearest Neighbor Classifiers, 5.3–Bayesian Classifiers (Complete for discrete data and only introduction of Bayes classifier for continuous attributes) till pg. 233, 5.7.1 –Alternative Metrics
		+	\| [https://www-users.cse.umn.edu/~kumar001/dmbook/index.php#item4 Read from Authors' web page]
		+	\| Chapter 5 (CB1)
		+	\|-
		+	\| Unit 6 / Chapter 8
		+	\| '''''Clustering''''': 8.1 Basic concepts of clustering analysis, 8.2 K-Means (8.2.1-8.2.5 except 8.2.3), 8.3 Agglomerative Hierarchical Clustering (except pg 522-524), 8.4 DBSCAN
		+	\| [http://mkbhandari.com/mkwiki/data/spring2023/DM/5CA.pdf '''5CA.pdf''']
		+	\| Chapter 8 (CB1)
		+	\|}
		+
		+	== Assignments and Tests==
		+	===Class Assignments===
		+	* '''''Assignment No. 1''''',
		+	* '''''Assignment No. 2''''',
		+
		+	===Tests and Quizzes===
		+	* '''Test 1''' :
		+	* '''Test 2''' :
		+
		+	== Resources ==
		+	===Course Books:===
		+	* '''CB1''': Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson Education. <br>
		+	===References:===
		+	* '''R2''': Data Mining: Concepts and Techniques, 3nd edition,Jiawei Han and Micheline Kamber. <br>
		+	* '''R3''': Data Mining: A Tutorial Based Primer, Richard Roiger, Michael Geatz, Pearson Education 2003. <br>
		+	* '''R4''': Introduction to Data Mining with Case Studies, G.K. Gupta, PHI 2006. <br>
		+	* '''R5''': Insight into Data mining: Theory and Practice, Soman K. P., DiwakarShyam, Ajay V., PHI 2006