This course aims to introduce students to Pattern recognition scientific discipline whose goal is the classification of objects into a number of categories or classes. These objects can be images or signal waveforms or any type of measurements that need to be classified. Topics that will be covered include:, Nearest neighbor approach (KNN), Decision trees and Random Forest, Bayes decision theory and probability essentials, Gaussian classifier, Gaussian mixture models and clustering (including K-means), Hidden Markov models (and speech recognition basics), Linear classifiers , Multilayer perceptron neural network, Support vector machines.
This course provides an introduction to computers and computing .Topics of interest include the impact of computers on society, ethical issues, and hardware /software applications, including internet applications, system unit, storage and input/output devices, numbering systems, system and application software, presentation skills, program development, programming languages, and flow charts, Visual Basic, web page design using HTML, and communications and networks.
This course will help students know the concepts of programming using VB, and knowing how to use editors to implement these concepts. Topics include: Data types and variables, operators, various Visual Interface Controls, conditional structures such as If and select statements, different forms of repeatition statements such as While and For, arrays, functions and procedures.
This course teaches students OO modeling and development concepts using Java programming languages. Topics include: classes – objects – Strings, File I/O, inheritance – abstraction, interfaces, Exception handling, and GUI interfaces.
This course teaches students concepts of distributed and parallel systems. Topics include: Distributed systems and applications - Recent developments in distributed systems - Client-server and peer-to-peer application designs - Sockets - Reliability - Replication - Group membership protocols - Clock synchronization - logical timestamps.