<a id="ctl00_PlaceHolderSiteName_onetidProjectPropertyTitle" href="/departments/ics/CSRG/">CSRG

Catalog Description:

Basic database concepts, conceptual data modeling, relational data model, relational theory and languages, database design, SQL, introduction to query processing and optimization, and introduction to concurrency and recovery.

Textbook(s):

Fundamentals of Database Systems, R. Elmasri and S. B. Navathe, 5th edition, Addison-Wesley, 2006.

Reference(s) and Other Material:

Database System Concepts, by Abraham Silberschatz, Henry F. Korth, S. Sudarshan McGraw Hill, 5th
Edition, 2006

Course Outcomes:

After completion of this course, the student shall be able to:

Explain the basic database concepts and relational theory.
Understand and develop relational / conceptual data model.
Use and apply database programming languages and physical database design.
Understand the basics of query processing and optimization, concurrency controls and database recovery.
Gain work experience in a lab project as a team member or leader.

Topics Covered:

Information models and systems
The relational data model
Relational languages
SQL
Conceptual modeling and mapping
Functional dependency and normalization
Practical and Physical database design Concepts
Database processing issues like query processing and optimization, concurrency controls, security, and database recovery

Course Information

Class/Laboratory Schedule:

Class/Laboratory Schedule: Three 50 minutes lectures, One 180 minutes lab per week (3-3-4)

Designation:

Required Course

Course Level:

Undergraduate

Prerequisites

Prerequisite(s) by Topic:

Algorithms and Problem-Solving
Recursion
Fundamental Computing Algorithms
Virtual Machines
Declarations and types
Abstraction Mechanisms
Object-Oriented Programming Paradigm
Using APIs

Prerequisite Courses:

Catalog Description:

Review of object-oriented concepts; Introduction to design patterns; Basic algorithms analysis; Fundamental data structures - implementation strategies for stacks, queues and linked lists; Recursion; Implementation strategies for tree and graph algorithms; Hash tables; Applications of data structures (e.g. data compression and memory management).

Textbook(s):

Data Structures and Algorithms in Java, 2nd Edition, Adam Drozdek, Thomson Learning, 2005

Reference(s) and Other Material:

Data Structures and Algorithms with Object Oriented Design Patterns in Java, Bruno R. Preiss, John Wiley & Sons, Inc., 2000.

Course Outcomes:

Apply object oriented concepts (inheritance, polymorphism, design patterns, etc.) in software design.
Implement various data structures and their algorithms, and apply them in implementing simple applications.
Analyze simple algorithms and determine their efficiency using big-O notation.
Apply the knowledge of data structures to other application domains like data compression and memory management.

Topics Covered:

Review of Object-Oriented Concepts & Introduction to Design Patterns
Introduction to Algorithm Analysis.
Linked Lists.
Stacks & Queues.
Recursion.
Binary Trees, Binary Search Trees & Tree Traversal Algorithms.
Binary Heaps.
AVL Trees, B-Trees, B+-Trees.
Graphs, Graph Traversal Algorithms & Graph Implementations.
Graph Algorithms (Connectedness, Cycle detection & Topological sort).
More Graph Algorithms (Shortest path & Minimum Spanning Trees).
Hashing Techniques.
Application of Data Structures: Data Compression.
Application of Data Structures: Memory Management.

ICS 102: Introduction to Computing I

ICS 201: Introduction To Computing II

Course Information

Class/Laboratory Schedule:

Three 50 minutes lectures, One 180 minutes lab per week (3-3-4)

Designation:

Required Course

Course Level:

Undergraduate

Prerequisites

Prerequisite(s) by Topic:

Fundamental programming constructs.
Algorithms and problem-solving.
Fundamental data structures.
Declarations and types.
Object-oriented programming.

Prerequisite Courses:

Catalog Description:

Advanced object-oriented programming; inheritance; polymorphism; abstract classes and interfaces, container and collection classes, packages, object-oriented design, software modeling, event-driven programming, recursion, use of stacks, queues and lists from API, searching and sorting.

Textbook(s):

Absolute Java, 2nd Edition, Walter Savitch, Addison-Wesley, 2006.

Course Outcomes:

Develop solutions for a range of problems using object-oriented programming
Apply divide and conquer strategy to searching and sorting problems using iterative and/or recursive solutions.
Design and implement simple GUI applications
Write simple multi threaded applications.
Use API in writing applications.

Topics Covered:

Inheritance and Polymorphism
Abstract Classes and Interfaces
Nested Classes
Java Virtual Machine
Algorithms, Problem Solving and Recursion
Searching and Sorting
Collections
Computer Graphics and Event Driven Programming
Multithreading

ICS 102: Introduction to Computing I

ICS 102: Introduction To Computing I

Course Information

Class/Laboratory Schedule:

Two 50 minutes lectures, One 180 minutes lab per week (2-3- 3)

Designation:

Required Course

Course Level:

Undergraduate

Prerequisites

Prerequisite(s) by Topic:

No programming or computer science experience is required.
Good prep-year mathematics should be sufficient to appreciate the software solutions of basic mathematical problems.

Catalog Description:

Overview of computers and computing. Introduction to a typical object-oriented programming language. Basic data types and operators. Basic object-oriented concepts. Wrapper classes. Console input/output. Logical expressions and control structures. Classes and methods. Arrays and strings.
Co-requisite: MATH: 101 Calculus I or MATH 132: Applied Calculus

Textbook(s):

Absolute Java, 2nd Edition, Walter Savitch, Addison-Wesley, 2006.

Reference(s) and Other Material:

Eric Jendrock, Jennifer Ball, Debbie Carson, Ian Evans, Scott Fordin, and Kim Haase. The Java™ EE 5 Tutorial. Addison Wesley Professional, 2007.
Elliot B. Koffman and Ursula Wolz, Problem Solving with Java, Update, 2/E. Addison-Wesley, 2002.

Course Outcomes:

Write programs using basic data types and strings,
Design and implement programming problems using selection,
Design and implement programming problems using loops,
Use and implement classes as data abstractions in an object-oriented approach,
Implement simple exception handling in programs,
Develop programs with input/output from text files,
Design and implement programming problems involving arrays.

Topics Covered:

Introduction to Computer Systems and Computing.
Object-Oriented Programming Basics.
User Input Handling and Decisions.
Input/Outputs Using Text Files.
Objects.
String Objects.
Class Design.
Data Encapsulation in Object-Oriented Programming.
Input/Output Arguments in Methods.
String Tokenization.
Arrays and Multidimensional Arrays.
Exception Handling.

MATH: 101 Calculus

MATH 101: Calculus I

Course Information

Designation:

Other

Course Level:

Undergraduate

Catalog Description:

Limits and continuity of functions of a single variable. Differentiability. Techniques of differentiation. Implicit differentiation. Local extrema, first and second derivative tests for local extrema. Concavity and inflection points. Curve sketching. Applied extrema problems. The Mean Value Theorem and applications.

This course is offered by the Department of Mathematics and Statistics more information can be found on their website.

MATH 132: Applied Calculus

Course Information

Designation:

Required Course

Course Level:

Preparatory Year

Catalog Description:

The derivative. Rules for differentiation. Derivative of logarithmic, exponential, and trigonometric functions. Differentials. Growth and decay models. Definite and indefinite integrals. Techniques of integration. Integrals involving logarithmic, exponential and trigonometric functions. Integration by tables. Area under a curve and between curves. Functions of several variables. Partial derivatives and their applications to optimization.

This course is offered by the Department of Mathematics and Statistics more information can be found on their website.

Junior Standing

Course Information

Designation:

Other

Course Level:

Undergraduate

Catalog Description:

According to KFUPM academic regulations a Computer Science or Software Engineering major student is considered as "Junior Standing" if the student has successfully passed and completed between 65 to 100 credit hours in his specialized major. More information about classifications can be obtained from the KFUPM Registrar's Website.

SWE 311: Principles of Software Engineering

SWE 311: Principles Of Software Engineering

Course Information

Class/Laboratory Schedule:

Three 50 minutes lectures, One 180 minutes lab per week (3-3-4)

Designation:

Required Course

Course Level:

Undergraduate

Prerequisites

Prerequisite(s) by Topic:

Fundamental programming constructs
Fundamental data structures

Prerequisite Courses:

ICS 233: Computer Architecture & Assembly Language

Catalog Description:

History and overview of software engineering. Software processes. Software project management. Software requirements and specification. Software design. Software testing and validation. Software metrics. Software quality assurance. Software evolution. Using APIs. Software tools and environments.

Textbook(s):

Somerville, Ian. Software Engineering, 8th Edition, Addison-Wesley, 2006.

Reference(s) and Other Material:

Pressman, R. S. Software Engineering: A Practitioner's Approach, 6th Ed., McGraw- Hill Companies, Inc., 2005.

Course Outcomes:

After completion of this course, the student shall be able to:

Select an appropriate and effective software process of a given project.
Develop clear, concise, and sufficiently formal software requirements specification (SRS) based on the true needs of users and other stakeholders.
Apply design principles and architectures in designing software.
Create a number of different UML models such as class model, use-cases, sequence diagram, activity diagram, and state charts.
Develop a project plan for software development project and apply management techniques and work as a team leader and a member of a team

Topics Covered:

Software design
Software tools and environments
Software processes
Software requirements and specifications
Software Validation
Software Evolution
Software project management
Component-based Computing
Foundations of human-computer interaction

Course Information

Class/Laboratory Schedule:

Class/Laboratory Schedule: Three 50 minutes lectures, One 180 minutes lab per week (3-3-4)

Designation:

Required Course

Course Level:

Undergraduate

Prerequisites

Prerequisite(s) by Topic:

Fundamental programming constructs
Digital logic and digital systems
Machine level representation

Prerequisite Courses:

COE 202: Digital Logic Design

COE 202: Digital Logic Design

Catalog Description:

Machine organization; Assembly language: addressing, stacks, argument passing, arithmetic operations, decisions, modularization; Input/Output Operations and Interrupts; Memory Hierarchy and Cache memory; Pipeline Design Techniques; Super-scalar architecture; Parallel Architectures.

Textbook(s):

Computer Organization and Design: The Hardware / Software Interface. Third Edition. David A. Patterson and John L. Hennessy. Morgan Kaufmann, 2004.

MIPS Assembly Language Programming. Robert L. Britton. Pearson Prentice Hall, 2004. (Laboratory textbook)

Course Outcomes:

Analyze, write, and test MIPS assembly language programs.
Describe the organization and operation of integer and floating-point arithmetic units.
Apply knowledge of mathematics in CPU performance and in speedup computation.
Design the datapath and control of a processor.
Use simulator tools in the analysis of assembly language programs and in CPU design.

Topics Covered:

Data Representation
Instruction Set Architecture
MIPS Assembly Language Programming
Procedures and the Runtime Stack
Integer and Floating-point Arithmetic and ALU design
MIPS floating-point coprocessor and instructions
CPU Performance
Single-Cycle Datapath and Control Design
Pipelined Datapath and Control
Memory System Design

Course Information

Designation:

Required Course

Course Level:

Undergraduate

Catalog Description:

Introduction to information representation and number systems. Boolean algebra and switching theory. Manipulation and minimization of completely and incompletely specifiedBoolean functions. Physical properties of gates: fan-in, fan-out, propagation delay, timing diagrams and tri-state drivers. Combinational circuits design using multiplexers, decoders, comparators and adders. Sequential circuit analysis and design, basic flip-flops, clocking and timing diagrams. Registers, counters, RAMs, ROMs, PLAs, PLDs, and FPGA's.

This course is offered by our sister Department, the Computer Engineering Department. More information about this course can be obtained from their website.

ICS 102: Introduction to Computing I

Course Information

Class/Laboratory Schedule:

Class/Laboratory Schedule: Three 50-minute lectures per week. No lab (3-0-3)

Designation:

Required Course

Course Level:

Undergraduate

Prerequisites

Prerequisite(s) by Topic:

Introduction to Computing.
Algorithms and problem solving

Prerequisite Courses:

Catalog Description:

Propositional Logic, Propositional Equivalence, Predicates and Quantifiers, Nested Quantifiers, Rules of Inference, Introduction to Proofs; Sets, Set Operations, Functions, Sequences and Summations; Mathematical Induction, Strong Induction, Recursive Definitions and Structural Induction; The Basics of Counting, The Pigeonhole Principle, Permutations and Combinations, Binomial Coefficients, Generalized Permutations and Combinations; Discrete Probability, Probability Theory; Recurrence Relations, Solving Linear Recurrence Relations, Generating Functions, Inclusion-Exclusion; Graphs and graph Models, Graph Terminology and Graph Isomorphism, Connectivity, Euler and Hamilton Paths, Planar Graphs, Graph Coloring; Introduction to Trees, Applications of Trees, Spanning Trees.

Textbook(s):

Rosen, Kenneth H. Discrete Mathematics and Its Applications, 6th Edition. New Your, McGraw Hill, 2007.

Course Outcomes:

Formulate and derive propositional/predicate logic expressions, and apply proving methods.
Apply counting techniques to solve combinatorial problems.
Comprehend graphs and trees and their mathematical properties.

Topics Covered:

Functions, relations, and sets
Basic logic
Proof techniques
Basics of counting
Graphs and Trees

ICS 254: Discrete Structures II

Course Information

Class/Laboratory Schedule:

Class/Laboratory Schedule: Three 50 minutes lectures per week. No lab (3-0-3)

Designation:

Required Course

Course Level:

Undergraduate

Prerequisites

Prerequisite(s) by Topic:

Functions, Relations and Sets
Proof Techniques
Counting Techniques

Prerequisite Courses:

K. H. Rosen, Discrete Mathematics and Its Applications, 6th Ed., McGraw-Hill, 2007.

Catalog Description:

Number Theory: Modular Arithmetic, Integer Representation, Fermat’s Little Theorem. Chinese Remainder Theorem, RSA., Applications from Number Theory; Recursive Definitions; Algorithm Correctness; Relations: Closures and Equivalence Relations, Partial Orderings and Lattices, Hasse Diagrams; Automata Theory: Finite State Machines, Regular Expressions, DFA, NFA and their equivalence, Grammars and Chomsky Hierarchy; Abstract Algebra: Groups, Homomorphism and Lagrange's Theorem, Applications.

Textbook(s):

Reference(s) and Other Material:

N. L. Biggs, Discrete Mathematics (revised edition), Clarendon Press, 1989.
Crisler, N., Fisher, P. and Froelich, Discrete Mathematics through Applications, 2nd Ed., W. H. Freeman Co., 2000.
R. P. Grimaldi, Discrete and Combinatorial Mathematics: An Applied Introduction, 4th Ed., Addison Wesley, 1998.

Course Outcomes:

Explain basic concepts in number theory and apply them in problem-solving.
Understand relations and their graphical representation
Understand foundational knowledge of group theory and automata theory

Topics Covered:

Number Theory
Induction & Recursion
Relations
Group Theory
Languages, Grammars and Finite State Machines

ICS 309: Computing And Society

Course Information

Class/Laboratory Schedule:

2 lectures per week, 50 minutes each (2-0-2).

Designation:

Required Course

Course Level:

Undergraduate

Prerequisites

Prerequisite(s) by Topic:

No programming or computer science experience is required.
Good background in high-school and prep-year English should be sufficient to appreciate the solutions of basic ethical problems.

Prerequisite Courses:

Junior Standing

Catalog Description:

Impact of Computing on Society; Ethical Foundations; Governance and Regulation; Freedom of Speech; Intellectual Property; Privacy; Security; Professional Responsibility; Leadership challenge.

Textbook(s):

Ethics in Information Technology by George Reynolds, 2nd Edition, Thomson Course Technology, 2007.

Reference(s) and Other Material:

A Gift of Fire by Sara Baase. 2nd Edition. Pearson Education Inc., 2003.
Morality and Machines by Stacy Edgar. 2nd Edition. Jones and Bartlett, 2003.
The Leadership Challenge by James Kouzes, and Barry Posner. 3rd Edition. Wiley (Jossey-Bass), 2002.

Course Outcomes:

After completion of this course, the student shall be able to:

Describe and evaluate consequences of computing on individuals, organizations, and society.
Critically analyze situations of computer use and technology and policy proposals, identifying the salient issues and evaluating the reasoning about them.
Understand philosophical frameworks of ethics.
Communicate clearly with others, in writing and in speech, about computing impacts.
Describe the characteristics of a good leader.

Topics Covered:

Ethics for IT professionals and IT Users
Computer and Internet Crime
Privacy
Freedom of Expression
Intellectual Property
Computers and Work
Employer Employee Issues
Impact of IT on quality of life

ICS 343: Fundamentals Of Computer Networks

Course Information

Class/Laboratory Schedule:

Three 50 minutes lectures, One 180 minutes lab per week(3-3-4)

Designation:

Required Course

Course Level:

Undergraduate

Prerequisites

Prerequisite(s) by Topic:

Algorithms and Problem-Solving
Recursion
Fundamental Computing Algorithms
Multithreading Programming
Abstraction Mechanisms
Object-Oriented Programming Paradigm
Using APIs

Note: This course cannot be taken for credit with COE 344.

Prerequisite Courses:

Data Communications and Networking, 4/e. Behrouz A. Forouzan, McGraw-Hill Higher Education 2007, ISBN-13: 978-007-125442-7.

Catalog Description:

This course provides a comprehensive and current introduction to computer networks with focus on the functions performed at each layer of the network architecture and common layer protocol standards.

Note: This course cannot be taken for credit with COE 344.

Textbook(s):

Lab Manual. Available through WebCT.

Reference(s) and Other Material:

Computer Networking: A Top Down Approach Featuring the Internet, 4/e, J. Kurose & Keith Ross, Addison Wesley, 2008.
Computer Networks: A Systems Approach, 3/e, Larry L. Peterson, Bruce S. Davie, Morgan Kaufmann Publishers, 2003.

Course Outcomes:

After completion of this course, the student shall be able to:

Identify various network services, characteristics, elements, standards and technologies.
Describe the layered architecture of computer networks and the operation of main protocols in the TCP/IP model.
Identify, compare and contrast different techniques and design issues of core functions such as addressing, routing, internetworking, switching, multiplexing, error and flow control, medium access and coding.
Implement simple client-server applications using socket programming.
Effectively use commonly used network-related commands, monitoring tools, traffic analyzers and network simulators.
Demonstrate the ability to setup a small network and properly configure network components including switches, routers and services (such as RAS, FTP, DNS, Web, DHCP, POP3).
Explain potential threats to network resources and various security mechanisms.

Topics Covered:

Introduction to computer networks and layered architectures, connectivity, topology, circuit and packet switching
TCP/IP and ISO models
Application layer: C/S model, DNS, SMTP, FTP, WWW,
Socket programming and network security;
Transport layer: TCP and UDP, congestion control;
Network layer: internetworking, addressing and routing algorithms and protocols
Data link layer: framing, flow and error control protocols, PPP, MAC and LANs;
Physical layer: principles of data communications, circuit switching, coding, multiplexing and transmission media.

ICS 353: Design And Analysis Of Algorithms

Course Information

Class/Laboratory Schedule:

Three 50-minute lectures per week. No lab (3-0-3)

Designation:

Required Course

Course Level:

Undergraduate

Prerequisites

Prerequisite(s) by Topic:

Basic Algorithmic Analysis
Fundamental Data Structures and Algorithms
Recursion
Proof Techniques
Basic of Counting

Prerequisite Courses:

ICS 381: Principles Of Artificial Intelligence

Catalog Description:

Algorithms and Problem Solving Basic Algorithmic Analysis; Advanced Algorithmic Analysis; Advanced Data Structures Algorithms Strategies & Analysis of Fundamental Computing Algorithms; Basic Computability; The Complexity Classes P and NP.

Textbook(s):

M. Alsuwaiyel, Algorithms, Design Techniques and Analysis, World Scientific, 1999.

Reference(s) and Other Material:

Course Outcomes:

After completion of this course, the student shall be able to:

Analyze the complexity of a given algorithm.
Apply classical sorting, searching, optimization and graph algorithms.
Compare, contrast, and choose appropriate algorithmic design techniques to present an algorithm that solves a given problem.
Able to explain NP-Completeness and deal with NP-complete problems.

Topics Covered:

Basic Concepts in Algorithmic Analysis
Heaps and Disjoint-Sets Data Structures
Solving Recurrence Relations: Expanding the recurrence, Change of Variable, and the Master Theorem.
Divide and Conquer: Recursive Algorithms for Sorting, selection, Multiplication of Large Numbers and Matrices, Closest Pair Problem.
The Greedy Approach: Fractional Knapsack, Activity Selection, Money Change, Longest Common Subsequence, Matrix-Chain Multiplication, all-Pair Shortest Paths.
NP-Complete Problems.

Course Information

Class/Laboratory Schedule:

3 * 50-minute lectures per week. No lab. (3-0-3)

Designation:

Required Course

Course Level:

Undergraduate

Prerequisites

Prerequisite(s) by Topic:

Basic concepts in discrete structures, probability, calculus, and linear algebra.
Programming Fundamentals.

Prerequisite Courses:

ICS 410: Programming Languages

Catalog Description:

Introduction to Artificial Intelligence (AI) history and applications; First order logic; State space representation; Blind and heuristic search; Constraint satisfaction and planning; Knowledge representation; Reasoning in uncertain situations; Machine learning; Prolog programming; Natural language processing, Advanced AI applications.

Textbook(s):

Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, Prentice Hall, (Second Edition) 2003.

Reference(s) and Other Material:

Artificial Intelligence: Structures and Strategies for Complex Problem Solving, George F. Luger, Addison Wesley Publisher, (Fifth Edition) 2005.
Prolog Programming for Artificial Intelligence, Ivan Bratko, Addison Wesley, (Third Edition) 2001.

Course Outcomes:

After completion of this course, the student shall be able to:

Understand the meaning of AI, its alternative approaches and the implications of AI for cognitive science more broadly.
Expand their knowledge about mechanisms, semantic networks, frame systems, heuristic search, genetic algorithm, planning, and symbolic learning algorithms.
Understand the basic methods in planning and reasoning using both logic and uncertain inference.
Know a variety of ways to represent and retrieve knowledge and information [Expert
systems, Agents].
Know the fundamentals of AI programming techniques and advanced machine learning in a modern programming language.

Topics Covered:

AI history and applications.
Intelligent Agents.
Problem Solving by Searching.
Constraint Satisfaction Problems.
Informed Search and Exploration.
Adversarial Search
First Order Logic.
Inference in First Order Logic.
Knowledge Representation and Knowledge-Base System.
Planning Systems.
Reasoning in Uncertain Situations.
Machine Learning.
Languages and Programming Techniques for AI (Prolog, Lisp).
Natural Language Processing.
Communicating, Perceiving, and Acting.
Advanced Applications of AI.

Course Information

Class/Laboratory Schedule:

Three 50 minutes lectures per week (3-0-3)

Designation:

Required Course

Course Level:

Undergraduate

Prerequisites

Prerequisite(s) by Topic:

Fundamental Programming Constructs
Fundamental Data Structures

Prerequisite Courses: