Course descriptions

Design and analysis of fault-tolerant digital systems. fault models, hardware redundancy, information redundancy, evaluation techniques, system design procedures.

Introduction to computer security and privacy. Topics include security in programs, operating systems, networks and databases.

Introduction to machine learning with images and natural text. A coverage of machine learning and data mining techniques such as decision trees, random forests, neural networks, kNN, Adaboost, stochastic gradient descent, regression, and transformers.

Robot sensors and actuators, motors, robot path planning, simultaneous localization and mapping, and robot vision.

A coverage of deep neural networks including Alexnet, convolutional neural networks, long short term memory networks, recurrent neural networks, and Resnet.

Image information, image filtering, template matching, histogram transformations, edge detection, boundary detection, region growing and pattern recognition. Complementary laboratory exercises are required.

Image and video processing lab experiments including pattern recognition, filtering, and edge detection.

Introduction to software engineering. Topics include software development life cycle, software processes, requirements engineering, software architecture and design, software testing, software evolution, and software modeling using UML

Magnetics and magnetically coupled circuits, electromechanical energy conversion, rotating magnetic fields, stepper motors, DC machines, induction machines, synchronous machines and brushless DC machines.

Introduction to computer science and programming. Topics include information and data representation, hardware, programming methodology, algorithm design, abstract data types, programming language, operating systems, applications and communications.

Introduction to application development. Topics include: basics of problem solving techniques, game programming, mobile applications.

An introductory course to problem solving and computational thinking using computer programming. Topics include: problem solving fundamentals, programming basics, variables and expressions, input/output, control structures, functions, strings and files.

Web design practices using modern technologies. Topics include: Website basic elements, Web page design, creating site content, and launching a web site.

Programming fundamentals from an object oriented design perspective. Topics include: basic procedural concepts, objects, classes, interface, methods, encapsulation, information hiding, and inheritance.

Advanced programming techniques using data structures. Topics include recursion, lists, stacks, queues, trees, graphs, and hash tables.

Object oriented analysis, design, and advanced programming concepts. Topics include: polymorphism, abstract classes, interfaces, exception handling, graphical user interface, and modelling notations.

Introductory course in digital logic and its specification and simulation. Number systems. Binary arithmetic. Boolean algebra. Combinational logic design. Sequential logic design (controllers). Data Path components (e.g. Registers, adders, register files , etc.). Memory components (e.g. RAM, ROM). Register transfer level (RTL) design.

Practicing advanced problem solving techniques: arrays, stacks, queues, strings, sorting, arithmetic, algebra, combinatorics, number theory, backtracking, geometry, graphs algorithms and traversals. Introduction to dynamic programming, preparing students majoring in computer science for international and regional ACM programming contests.

Computer architecture and organization. Topics include performance evaluation, instruction, set architecture, data path, control unit, pipelining, memory hierarchy, storage, and other I/O topics.

Introduction to systems programming in Unix/Linux environment. Topics include scripting, libraries, utilities, development tools, piping, binary files, exception handling, command-line arguments, and symbolic debugging.

Exploration of the Unix operating system, including its tools and utilities for program development, such as makefile, piping and redirection, shell scripts, regular expressions, and symbolic debuggers. In addition, this course explores advanced features of the C programming language, including various file processing, command-line and variable arguments, exception handling, and generic interfacing.

Introduction to Data Science. Topics include data acquisition, data management, data preparation and integration, data analysis, model development and deployment, and visualization.

This course introduces discrete structures. Topics include: basic logic, sets, relations, functions, proof techniques, counting methods, recursion, trees and graphs.

Introduction to web development. Topics include client/server side scripting, dynamic websites, web services, database connectivity, security, and authentication.

Design & analysis of recursive and non-recursive algorithms. Topics include asymptotic analysis of algorithm efficiency, algorithm design techniques (e.g. divide-and-conquer, dynamic programming, greedy approaches) and their applications to computational problems, and introduction to parallel algorithms.

Principles of programming language design. Specification of syntax and semantics. Underlying implementation of block structured languages. Dynamic memory allocation for strings, lists and arrays. Imperative versus applicative programming. First order logic and logic programming. Lambda calculus and functional programming languages. Modern programming languages.

In-depth study of object-oriented design patterns. Topics include: creational patterns, structural patterns, and behavioral patterns. Pattern's intent, motivation, applicability, participants, collaborations, consequences, and implementations.

Introduction to software engineering. Topics include software development life cycle, software processes, requirements engineering, software architecture and design, software testing, software evolution, and software modeling using UML.

Introduction to modern business analysis. Topics include: planning and monitoring, elicitation, requirements, analysis, solution assessment and validation, requirements management and communication, business problems identification, business cases development and plans for achieving effective solutions.

Fundamentals of Database Management Systems. Modern database models. Basic database management concepts. Query languages. Relational algebra and relational calculus. Database design through normalization. Development experience in a team environment.

Introduction to mobile application development. Topics include creating and deploying mobile applications, development approaches, programming languages, frameworks, application architecture, event-driven programming, model-view-controller, user interface and usability.

Internship in computer science provides students with an opportunity to integrate theory and practice in a work setting. The course helps students to grow professionally and intellectually. It also sharpens their communication and teamwork skills. Enrolled students are required to complete a minimum of 150 working hours at approved organizations.

Introduction to eCommerce. Topics include electronic data interchange, digital currency, electronic catalogs, telecommunications infrastructure, data warehousing, software agents, storage retrieval, cryptographic techniques, and different levels of security.

Introduction to user interface design principles and techniques. Topics include user and task analysis, interaction design, interaction elements and styles, design patterns, visual communication, prototyping, and evaluation of different user interfaces.

Techniques for software validation and verification. Topics include: concepts of software quality (metrics, assurance, planning & implementation, process standards, validation, verification, reviews, walkthroughs and inspections). Software testing process and techniques at different levels. Validating test data.

Introduction to software maintenance and re-engineering. Topics include evolution and maintenance models, re-engineering, legacy information systems, impact analysis, refactoring, program comprehension, principles of reuse and reverse engineering, approaches, and tools used to extract information from existing software systems.

Principles and techniques for designing and implementing a compiler. Topics include lexical analysis, parsing techniques, syntax-directed translation, intermediate languages, and code generation.

Survey of artificial intelligence techniques. Topics include search, logic, knowledge representation, reasoning with uncertainty, learning, and other AI techniques.

Introduction to digital image processing. Topics include Digital image fundamentals, intensity transformations, and spatial filtering, filtering in frequency domain, Image restoration and reconstruction, Color image processing, Image compression, and Image segmentation.

The senior capstone course provides computer science students the opportunity to employ knowledge gained from courses throughout the curriculum to develop a software solution to a real-world problem from conception to completion. Students work in teams to develop software applications. Advanced methods related to requirements analysis, software design, project management, documentation and testing, programming techniques, and database designs will be applied on an incremental basis since the emphasis is on semester-long capstone team projects.

This course is an in-depth study of software development processes in the context of an actual project. Discussion includes object-oriented processes, such as Rational Unified Process, as well as process management issues such as scheduling, risk-assessment, various metrics, and the selection of appropriate development methodology and tools.

Introduction to computer communications networks. Topics include circuit-switching, packet switching, TCP/IP model layers, and wireless networks.

Design & implementation of operating systems. Topics include basic operating system structure, process and thread management, deadlocks, memory management, file management, input / output management, and distributed systems.

Introduction to computer security and privacy. Topics include security in programs, operating systems, networks and databases.

Topics in Computer Science

Introduction to Academic Research in Computer Science. Topics include: Research techniques and approaches, Computer Science research areas and metrics, literature review, writing a research paper, referencing and citations, and developing an effective workflow.

Practicum in Computer Science prepares students for successful computing related careers. The course aims to train and certify the students with market demanding fields such as computer networks, database systems, computer system/ applications, business and leadership etc.

Introduction to ethics theory and ethical decision making within the computing profession context. Topics include local and global impact of computing solutions, intellectual property, privacy, network security, electronic crimes and computer reliability.

This course is an examination of the concepts, theorems and problems which are fundamental to an understanding of economic decision making. Emphasis is on the real-life examples of fundamental concepts and application of quantitative methods to the solution of individual decision-making problems of households, firms and industries.

This is an introductory course in macroeconomics. The course is designed to introduce students to the essential concepts of macroeconomics. The approach is both analytical and policy oriented. Macroeconomics looks at the big picture of the economy. The course focuses on understanding the main macroeconomic problems, such as recession, inflation, and unemployment. In addition, the course analyses how fiscal and monetary policies can be used to influence macroeconomic performance and long-term growth.