B.E. Electronics & Comm. Engg:Data Mining and Pattern Recognition
Bachelor
In Patiala
Description
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Type
Bachelor
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Location
Patiala
Facilities
Location
Start date
Start date
Reviews
Course programme
First Year: Semester I
Mathematics I
Engineering graphics
Computer Programming
Physics
Solid Mechanics
Communication Skills
First year: Semester II
Mathematics II
Manufacturing Process
Chemistry
Electrical and Electronic Science
Thermodynamics
Organizational Behavior
Second year: Semester I
Numerical and Statistical Methods
Measurement Science and Techniques
Electromagnetic Fields
Semiconductor Devices
Signals and Systems
Digital Electronic Circuits
Human Values, Ethics and IPR
Second year: Semester II
Optimization Techniques
Analog Electronic Circuits
Networks and Transmission Lines
Electrical Engineering Materials
Analog Communication Systems
Data Structure and Information Technology
Environmental Studies
Third year: Semester I
Digital Signal Processing for Communications Microprocessors
VLSI Circuit Design
Digital Communication Systems
Microelectronics Technology
Linear Integrated Circuits and Applications
Summer Training(6 weeks)
Third year: Semester II
Project Semester
Project
Industrial Training(6 weeks)
Fourth year: Semester I
Antenna and Wave Propagation
Modern Control Engineering
Wireless and Mobile Communication Systems
Microwave Engineering
Engineering Economics
Fourth year: Semester II
Optical Communication Systems
Advanced Communication Systems
HDL Based Digital Design
Total Quality Management
Minor Project
Data Mining and Pattern Recognition
Data Mining: What is data mining, on what kind of data, Data Mining Functionalities
Data Warehouse: Difference Between operational database systems and data warehouses, A multidimensional data model, Data Warehouse architecture, data warehouse architecture, Data Warehouse implementation
Data preprocessing: Data cleaning, data integration & transformation, data reduction
Data Mining Query Language
Characterization & Comparison, Generalization, Mining association rules in large databases, constraint based association Mining
Classification & prediction Classification by decision Tree Induction, Bayesian classification, classification by Back propagation
Cluster analysis Partitioning Methods, Hierarchical methods, and Density & Grid based methods
Mining complex types of data, applications & trends in data mining, Social impacts of data mining
Pattern recognition: its importance & applications, applications in Bioinformatics, recognizing important bioinformatic sequences, other applications of pattern discovery
Laboratory Work: Implementation of various data mining techniques like classification, clustering, generalization, cleaning etc.
B.E. Electronics & Comm. Engg:Data Mining and Pattern Recognition