B.E. Electrical Engineering:Data Mining and Pattern Recognition
Bachelor
In Patiala
Description
-
Type
Bachelor
-
Location
Patiala
-
Duration
4 Years
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
Semiconductor Devices
Measurement Science and Techniques
Digital Electronic Circuits
Circuit Theory
Electromechanical Energy conversion
Electromagnetic Fields
Human Values, Ethics and IPR
Second Year- Semester – II
Numerical and Statistical Methods
Fluid Mechanics
Power Generation and Economics
Analog Electronic Circuits
Electrical and Electronic Measurements
Transmission and Distribution of Power
Environmental Studies
Third Year- Semester – I
Power Electronics
Asynchronous Machines
Switch gear and Protection
Microprocessors
Optimization Techniques
Engineering Economics
Summer Training (6 Weeks after 2nd year during summer vacation)
Third Year- Semester – II
Total Quality Management
Control Systems
Synchronous Machines
Power System Analysis
Flexible AC Transmission Systems
Fourth Year- Semester – I
High Voltage Engineering
Operation and Control of Power Systems
Electric Drives
Intelligent Algorithms in Power Systems
Fourth Year- Semester – II
Project Semester
Project
Industrial Training
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. Electrical Engineering:Data Mining and Pattern Recognition