Advanced Data Mining Programme course 6 credits Avancerad Data Mining 732A75 Valid from: 2018 Spring semester Determined by Course and Programme Syllabus Board at the Faculty of Arts and Sciences Date determined 2018-03-20 DNR LIU-2018-00955 1(3) LINKPING UNIVERSITY FACULTY OF ARTS AND SCIENCES. Data warehousing, text, graph and probabilistic data mining, scalable clustering methods, association analysis, anomaly detection, management of personal integrity in the field of data mining. It is assumed that every student is familiar with the basic data mining topics syllabus MECS-701 Advanced Data Warehousing & Data Mining 4 - 4 73 MECS-703 Advanced Software Testing 4 - 4 74 Electives (Choose any Three) MEEC-707 Artificial Neural Networks 4 - 4 133 MECS- 705 Cloud Computing 4 - 4 75 MECS-707 E-Commerce & Applications 4 - 4 76 MECS-709 Information Storage & Management 4 - 4 77 .* Gain a working knowledge of data mining techniques 2. .* /CreationDate (D:20210503000142+02'00') .* /SA true .* >> The (*) modules are more advanced and can be skipped for a more introductory level course. /BitsPerComponent 8 /Height 493 WVU Syllabi. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. 19ECS701 Advanced Data Structures PC 3 0 0 3 2. 3 0 obj Introduction (2 hours) Data Mining Origin; Data Mining & Data Warehousing basics; Data Pre-Processing (6 hours ) Entry requirements. Please contact Dr. Hahsler if you have concerns or questions about this prerequisite. endobj .* Recommended Citation. .* .* .* Course code Data Mining Techniques L T P J C SWE2009 3 0 0 4 4 Pre-requisite SWE1004 Syllabus version v 1.0 Course Objectives: To understand the fundamental data mining methodologies and with the ability to formulate and solve problems. We will study some classic papers as well as some important recent papers, on different types of applications, in this fast evolving field. .* 19ECS703 Mathematical Foundations of Computer Science PC 3 0 0 3 3. The course will To familiarize students with basic data mining principles, modern data mining methods and tools, as well as advanced data mining applications, and to help students find jobs in fields related to data mining, machine learning, data science, data analytics, data management and Big Data In order to meet the general entry requirements for the course, you must have accomplished a minimum of 120 ECTS of university studies, out of which 60 ECTS in the areas of computer or system science. Description: In this course we will cover advanced topics in data mining. course covers the fundamentals of High Dimensional data clustering Types of cluster analysis and applications Meta data detection Correlation and Association Analysis Resolution of semantic heterogeneity towards smooth distributed data integration Implementation of Support CSC 566: Advanced Data Mining Spring 2017 Course Syllabus April 3, 2017 Instructor: Alexander Dekhtyar email: dekhtyar@calpoly.edu oce: 14-215 What When Where Lecture MW 4:10 6:00pm 14-257 Note: the class will not have a written nal exam, but the exam time will be used for student presentations. /SA true .* Lecture 12 : Bayes Classifier I 19ECS7XX Program Elective I PE 3 0 0 3 Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5) Entry requirements: 120 credits including Data Mining I. English language proficiency that corresponds to English studies at upper secondary (high school) level in Sweden ("English 6"). .* Course Information. endobj >> 1. /Type /XObject stream .* By Yanfang Ye, Published on 01/01/17. Computer System Design 2. 1 2 . /Filter /FlateDecode << .* H=sw7)gMTA2`f#_;,?\U Pwu7H.ux ^#$%_W'!_K1^_/y~{@WAeKq8s;\xA x ygD>xR x ;/~_Q5VWb /CA 1.0 /Producer ( Q t 4 . .* /AIS false m. It introduces the basic concepts, principles, methods, implementation techniques, and applications of data mining, with a focus on two major data mining functions: (1) pattern discovery and (2) cluster analysis. /Type /ExtGState Advanced Python Programming 3 0 0 3 3 30 70 100 4 PE II GR18D5006 GR18D5007 GR18D5008 1. 1 0 obj Object Oriented Modelling 3. << Syllabus for Data Mining II. The course provides knowledge to address various data science problems and datasets. Syllabus Course Content @Canvas We will introduce (a) the core data mining concepts and (b) practical skills for applying data mining techniques to solve real-world problems. endobj /Producer ( Q t 4 . Object Oriented Modelling 3. Mandatory course in the following programmes: Master Programme in Data Science, semester 1. .* Learn to design and implement algorithms to apply techniques in a practical fashion 3. The topics can be roughly classified along the following dimensions: pattern/model types, mining/analysis techniques, and data types. Advanced topics (e.g., recommender systems, outlier detection, graph mining, temporal min- .* 7. stream One thought on Syllabus Use the Calendar to access the attached slides of helping sessions Data Mining and Visualization said: December 31, 2014 at 10:28 pm [] for all student assignments will be updated on the course Google calendar. Syllabus for Advanced Machine Learning, Data Mining, and Artificial Intelligence. /Title () endobj Course coordinators are listed on the course listing for undergraduate courses and graduate courses. .* j-}s/:\{y}A1. )XM([K ^)xfUcp= ~W ]U px ^5;_xo3uxGJoz\F/(WH{{l # /ColorSpace /DeviceGray Recommended Citation. 2) .* .* WVU Syllabi. /Length 10 0 R 6. [/Pattern /DeviceRGB] Winter, 2008 Syllabus . xyU3cK%RTThQiE{)IMJZDDBN0f=>y;~?ys|ys @PHy 9Wdx sCP$y 9* Syllabus Course Content @Piazza Syllabus (Fall 2020) We will introduce (a) the core data mining concepts and (b) practical skills for applying data mining techniques to solve real-world problems. Lecture 6 : Rule generation; Lecture 7 : Classification; Lecture 8 : Decision Tree - I; Lecture 9 : Decision Tree - II; Lecture 10 : Decision Tree III; Lecture 11 : Decision Tree IV; Week 3. Course Introduction, Syllabus Overview What is Data Mining? Advanced Web Technology and Data Mining and Business Intelligence Lab detailed Syllabus Scheme for Master of Computer Applications (MCA), 2017 regulation has been taken from the University of Mumbai official website and presented for the MCA students. Access Chapter Wise Notes of Data Mining. 8 . /ColorSpace /DeviceGray Cluster analysis: density-based cluster, graph-based cluster. To classify data mining systems and understand methods for data gathering and data pre - processing. Links to related topics are written at the side of corresponding chapter inside [] brackets. .* /Creator ( w k h t m l t o p d f 0 . .* Association rule mining though data mining tools. .* /Creator ( w k h t m l t o p d f 0 . Computer System Design 2. .* H=sw7)gMTA2`f#_;,?\U Pwu7H.ux ^#$%_W'!_K1^_/y~{@WAeKq8s;\xA x ygD>xR x ;/~_Q5VWb This syllabus is to be used as a guideline only. 7) 2) /CA 1.0 >> %PDF-1.4 Week 1: M1: Introduction: Machine Learning and Data Mining Assignment 0: Data mining in the news (1 week) Week 2: M2: Machine Learning and Classification
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