BACHELOR OF COMPUTER SCIENCE (ARTIFICIAL INTELLIGENCE)
COURSE LEARNING OUTCOMES Bachelor of Computer Science (Artificial Intelligence) academic program is offered to prepare graduates with a thorough understanding and superior skills of Computer Science, particularly in Information & Communication Technology. Graduates will also be equipped with advance scientific knowledge and engineering skills in Artificial Intelligence to fulfil industrial needs especially in the field of ICT, robotics and manufacturing.
LEARNING OUTCOMES The aim of FTMK to conduct the Bachelor of Computer Science (Artificial Intelligence) programme is to produce students with the following characteristics: 1. 2. 3.
4.
5. 6. 7. 8. 9.
Able to obtain and apply knowledge in computer science and information technology. Able to analyse, design and develop ICT applications. Able to apply artificial intelligent techniques such as searching technique, fuzzy logic, neural network, evolutionary computing, machine learning, and intelligent agent when developing a system. Equipped with skills to develop a system individually or in a group based on artificial intelligence such as intelligent system, expert system, intelligent agent system and robotic system. Able to conduct research in the fields related and based on artificial intelligence. Able to think creatively and critically in problem solving and able to communicate effectively to deliver ideas. Able to contribute skills individually or in group in difference disciplines and domains. Able to present good personality, ethics, leadership and entrepreneurship skills. Able to perform continuous self learning to obtain knowledge and skills.
CAREER PROSPECTS A wide range of career opportunities in the field of computer science and information technology is open to graduates who specialized in artificial intelligence. Graduates specialized in artificial intelligence can also pursue their postgraduate studies. Among the career opportunities are listed below. a. b. c. d. e. f. g. h. i.
Knowledge Engineer Intelligent Systems or Expert Systems Developer Systems Analyst Systems Programmer Systems Designer Software Developer Software Consultant Computer Scientist Researcher
CURRICULUM STRUCTURE To be conferred the Bachelor of Computer Science (Artificial Intelligence) with honours, the student is required to accumulate a minimum of 120 credits from the following course components: Components University Compulsory Subjects Program Core Subjects Course Core Subjects Elective Subjects TOTAL
Credit Hours 18 72 24 6 120
UNIVERSITY COMPULSARY SUBJECTS BLHC 4042 BLHW 1702 BLHW 2712 BLHW 2402 BLHW 3402 BLHW 1722 BLHL 4032 BLHW 1012 BLHL 1 - - 2 BKK* - - - 1 BKK* - - - 1
PROGRAMME CORE SUBJECTS BACS 1253 BACS 1263 BACS 2213 BITP 1113 BITP 1123 BITP 1213 BITP 1323 BITP 3113 BITP 2213
(18 credits)
Entrepreneur Skills and New Business (Kemahiran Keusahawanan dan Perniagaan Baru) Islamic and Asian Civilizations (Tamadun Islam dan Tamadun Asia –TITAS) Etnique Relation (Hubungan Etnik) Technical Communication I (Komunikasi Teknikal I) Technical Communication II (Komunikasi Teknikal II) Philosophy of Science and Technology (Falsafah Sains dan Teknologi) Critical and Creative Thinking (Pemikiran Kritis dan Kreatif) Foundation English (Asas Bahasa Inggeris)* Third Language (Bahasa Ketiga) Co-Curriculum I (Kokurikulum I) Co-Curriculum II (Kokurikulum II)
(72 credits)
Mathematics for Computer Science I (Matematik Sains Komputer I) Mathematics for Computer Science II (Matematik Sains Komputer II) Statistic and Probability (Statistik dan Kebarangkalian) Programming Technique (Teknik Pengaturcaraan) Data Structure and Algorithm (Struktur Data dan Algoritma) System Development (Pembangunan Sistem) Database (Pangkalan Data) Object Oriented Programming (Pengaturcaraan Berorientasikan Objek) Software Engineering (Kejuruteraan Perisian)
BITS 1123 BITS 1213 BITS 1313 BITS 2513 BITM 1113 BITM 2113 BITI 1113 BITU 2913 BITU 3923 BITU 3926 BITU 3946 BITU 3973 BITU 3983
Computer Organization and Architecture (Organisasi dan Senibina Komputer) Operating System (Sistem Pengoperasian) Data Communication and Networking (Komunikasi Data dan Rangkaian) Internet Technology (Teknologi Internet) Multimedia System (Sistem Multimedia) Web Application Development (Pembangunan Aplikasi Web) Artificial Intelligence (Kepintaran Buatan) Workshop I (Bengkel I) Workshop II (Bengkel II) Industrial Training (Latihan Industri) Industrial Training Report (Laporan Latihan Industri) Project I (Projek Sarjana Muda I) Project II (Projek Sarjana Muda II)
COURSE CORE SUBJECTS BITI 2113 BITI 2223 BITI 2213 BITI 3123 BITI 3133
(24 credits)
Logic Programming (Pengaturcaraan Logik) Machine Learning (Pembelajaran Mesin) Knowledge Based System (Sistem Berasaskan Pengetahuan) Fuzzy Logic (Logik Kabur) Neural Networks (Rangkaian Neural)
BITI 3113 BITI 3143 BITS 3423
Intelligent Agents (Agen Pintar) Evolutionary Computing (Pengkomputeran Evolusi) Information Technology Security (Keselamatan Teknologi Maklumat)
ELECTIVE SUBJECTS
(6 credits)
Choose any two of the following. BITI 3513 BITI 3523 BITI 3413 BITI 3213 BITI 3313 BITM 3313
Artificial Intelligence in Manufacturing (Kepintaran Buatan dalam Pembuatan) Artificial Intelligence in Robotics and Automation (Kepintaran Buatan dalam Robotik & Automasi) Natural Language Processing (Pemprosesan Bahasa Tabi’e) Decision Support System (Sistem Bantuan Keputusan) Image Processing and Pattern Recognition (Pemprosesan & Pengecaman Imej) Computer Games Development (Pembangunan Permainan Komputer)
CURRICULUM STRUCTURE PER SEMESTER Year One (Semester I) Code BLHW 1012 BLHW 1722 BLHW 2712 BLHW 1702 BACS 1253 BITP 1113 BITP 1213 BITS 1123
Foundation English Philosophy of Science and Technology Etnique Relation Islamic and Asian Civilizations Mathematics for Computer Science I Programming Technique System Development Computer Organization and Architecture TOTAL
Year One (Semester II) Code BLHW 2402 BKK --BACS 1263 BITP 1123 BITP 1323 BITS 1213 BITI 1113
Subject
Subject
Technical Communication I Co-Curriculum I ** Mathematics for Computer Science II Data Structure and Algorithm Database Operating System Artificial Intelligence
Contact Hours Lecture Lab 2 1 2 0 2 0 2 0 2 2 2 2 2 2 2 2
Credit
Pre-requisite
2* 2 2 2 3 3 3 3 18
*Exemption for students
Contact Hours Lecture Lab 1 2 0 3 2 2 2 2 2 2 2 2 2 2
Credit
Pre-requisite
Contact Hours Lecture Lab 2 1 3 0 2 2 9 0 2 2 2 2 2 2
Credit
TOTAL Year Two (Semester I) Code BLHW 3402 BKK ---BACS 2213 BITU 2913 BITP 3113 BITS 2513 BITI 2113
Subject
Technical Communication II Co-Curriculum II ** Statistic and Probability Workshop 1 Object Oriented Programming Internet Technology Logic Programming TOTAL
**This subject can be taken in any semester.
2 1 3 3 3 3 3 18
2 1 3 3 3 3 3 18
with MUET
BLHW 1012
Pre-requisite BLHW 2402
BITP 1113, BITP 1123 BITP 1113, BITP 1123
BITI 1113
Year Two (Semester II) Code BLHL 4032 BITP 2213 BITM 1113 BITS 1313 BITI 2223 BITI 2213
Subject
Critical and Creative Thinking Software Engineering Multimedia System Data Communication and Networking Machine Learning Knowledge Based System
Contact Hours Lecture Lab 0 2 2 2 2 2 2 2 2 2 2 2
TOTAL Year Three (Semester I) Code BLHL ---BITU 3913 BITM 1313 BITI 3123 BITI 3113 BITI 3133
Subject
Contact Hours Lecture Lab 0 2 9 0 2 2 2 2 2 2 2 2
TOTAL
BLHC 4042 BITU 3973 BITS 3423 BITI 3143 BITI ---BITI ----
Subject
Contact Hours Lecture Lab 0 2 25* 0 2 2 2 2 2 2 2 2
TOTAL
BITU 3983
BITP 1323, BITI 1113
BITI 1113
Credit 2 3 3 3 3 3
Pre-requisite
BITU 2913 BITI 2213, BITP 1113 BITI 1113, BITP 3113 BITI 2223, BACS 1253 BITP 1113
17
Entrepreneur Skills and New Business Project I Information Technology Security Evolutionary Computing Elective 1 Elective 2
Year Three ( Special Semester) Code
2 3 3 3 3 3
Pre-requisite
17
Third Language Workshop II Web Application Development Fuzzy Logic Intelligent Agents Neural Networks
Year Three (Semester II) Code
Credit
Credit 2 3 3 3 3 3
Pre-requisite
BITU 3923 BITS 1213, BITS 1313 BITI 2223, BITP 3113
17
Subject
Project II
TOTAL * Equivalent to 9 hours of contact if carried out in normal semester.
Contact Hours Lecture Lab 0
25*
Credit 3 3
Pre-requisite BITU 3973
Year Four (Semester I) Code BITU 3926 BITU 3946
Subject
Industrial Training Industrial Training Report
Contact Hours Lecture Lab 0 0
24 24
TOTAL
Credit
Pre-requisite
6 6 12
Elective Subjects Code BITI 3513 BITI 3523 BITI 3413 BITI 3213 BITI 3313 BITM 3313
Subject Artificial Intelligence in Manufacturing Artificial Intelligence in Robotics and Automation Natural Language Processing Decision Support System Image Processing and Pattern Recognition Computer Games Development
Contact Hours Lecture Lab 2 2 2 2 2 2 2 2 2 2 2 2
Credit
Contact Hours Lecture Lab
Credit
3 3 3 3 3 3
Pre-requisite BITI 3123, BITI 3133
BACS 1263 BITI 2113 BITI 2213 BITI 1113, BACS 1253
Third Language Code BLHL 1012 BLHL 1022 BLHL 1112 BLHL 1122 BLHL 1212 BLHL 1222 BLHL 1312 BLHL 1322 BLHL 1412 BLHL 1422 BLHL 1512 BLHL 1522
Subject Bahasa Melayu I Bahasa Melayu II Bahasa Arab I Bahasa Arab II Bahasa Mandarin I Bahasa Mandarin II Bahasa Jepun I Bahasa Jepun II Bahasa Jerman I Bahasa Jerman II Bahasa Perancis I Bahasa Perancis II
2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2 2 2 2 2
Pre-requisite BLHL 1012 BLHL 1112 BLHL 1212 BLHL 1312 BLHL 1412 BLHL 1512
PROGRAMME CORE SUBJECTS
BACS 1263 Mathematics for Computer Science II (3,3,2) Learning Outcomes
BACS 1253 Mathematics for Computer Science I (3,3,2)
Upon completing this course, students should be able to:
Learning Outcomes
1.
Upon completing this course, students should be able to: 1. 2.
3.
Explain the concepts of fundamental Linear Algebra and Discrete Mathematic. Solve problems in Computer Science related to Linear Algebra and Discrete Mathematic theory using software. Solve application problems using appropriate techniques.
Synopsis This course covers two disciplines of mathematics namely Linear Algebra and Discrete Mathematics. The topics for Linear Algebra are linear equations, matrices, determinants, n vectors in R , real vector spaces, eigenvalues, eigenvectors, diagonalization and linear transformation. The topics for discrete mathematics include logic, sets, function, algorithms, integers, mathematical reasoning, counting, relations, graphs, trees and Boolean algebra.
2.
3.
Synopsis This course covers two disciplines of mathematics namely calculus and numerical analysis. The topics for calculus are derivatives, function, differentiation techniques, logarithmic function and exponents as well as its application, integration techniques, and multivariable functions. The topics for numerical analysis include Taylor polynomial, numbers, error, interpolation, numerical differentiation and integration as well as numercal solution for differential equation. References 1.
References 1. 2. 3. 4. 5.
Kolman, B. and Hill, D.R. Introductory Linear Algebra with Application, 7th edition. Prentice Hall 2001. H. Anton. Elementary Linear Algebra. 8th edition. McGraw Hill. 1995. David C.Lay. Linear Algebra and Its Applications 3rd edition. Addison Wesley 2003. Kenneth H. Rosen. Discrete Mathematics and Its Applications, 4th edition. McGraw-Hill 1998. Johnsonbaugh, R. Discrete Mathematics. Prentice Hall 2005.
Apply the knowledge and basic concepts of calculus and numerical analysis. Solve problems in Computer Science related to calculus and numerical analysis theory using software. Solve application problems using appropriate techniques.
2. 3. 4. 5.
Goldstein, L. J., David I. S. (2004). Calculus and Its Application. Prentice Hall. James Stewart (2003). Calculus. Thomson. Johnston, E.H., Mathews J.C. (2002). Calculus. Pearson Education. . Atkinson, K. (2004). Elementary Numerical Analysis. John Wiley & Sons, Inc. Richard L.B., J. Douglas Faires (2004). Numerical Analysis. Thomson.
BACS 2213
Statistic and Probability (3,2,2)
Learning Outcomes Upon completing this subject, students should be able to:
1. 2. 3.
Explain the concepts of fundamental statistics and probability. Solve problems in statistic inference related to hypothesis test using software. Solve application problems using appropriate statistic techniques.
Synopsis Students will be introduced to the concept of probability and inferential statistics. The course starts with Probability followed by Discrete Random Variables, Continuous Random Variables and Sampling Distribution. The main topics for Inferential statististics are Estimation, Hypothesis Testing, Estimation and Hypothesis Testing: Two Populations, Anova, Simple Linear Regression and Correlation. This course will also provide the students with some exposure to statistical software.
2.
3. 4.
5.
Sh. Sara, Hanissah, Fauziah, Nortazi, Farah Shahnaz, Introduction to Statistics & Probability A Study Guide (2008), Pearson – Prentice Hall Douglas C. Montgomery, George C.Runger, Applied Statistics and Probability for Engineers, 3rd Edition (2002), John Wiley Richard A. Johnson, Probability and Statistics for Engineers, 7th Edition (2005), Pearson Prentice Hall Jay L. Devore, Probability and Statistics for Engineering and the Sciences, 6th Edition (2000), Thomson – Duxbury David M Levine, Patricia P. Ramsey, Robert K. Smidt , Applied Statistics for Engineers and Scientists Using Microsoft Excel and MINITAB (2001),Prentice Hall
BITP 1113
This course introduces the students to the basic concepts of computer and programming techniques that includes program lifecycle variable, identifier, data type, operator, selection, repetition, function, array, string, file and pointer. References 1. 2.
3. 4.
References 1.
Synopsis
Programming Technique (3,2,2)
Learning Outcomes At the end of the lesson, students should be able to: 1. Explain basic principles of problem solving in Software Engineering. 2. Demonstrate basic principles of programming. 3. Develop basic construction of C++ language in building program.
5.
6. 7.
D.S Malik (2009), “C++ Programming from Problem Analysis to Program Design”, Cengage Learning. A.Forouzan, Behrouz, (2000), “A Structured Programming Approach Using C++”, Brooks/Cole Thomson Learning. H.M Deitel, P.J Deitel, (2005), “C++ How To Program”, Prentice Hall. Savitch, Walter, (2006),”Absolute C++”, Addison Wesley. Bronson, Gary J, (2000), “Program Development and Design Using C++”, Brooks/Cole Publishing Company. Knowlton, T, (2000), “Introduction To Computer Science Using C++”, Thomson Learning. Schildt, H, “The Single Easiest Way To Master C++ Programming”, Mc Graw Hill.
BITP 1123
Data Structure and Algorithm (3,2,2)
Learning Outcomes At the end of the lesson, students should be able to: 1. Identify suitable data structure for certain application. 2. Solve problems by applying knowledge in data structure and algorithm. 3. Analyze the memory and run time efficiency of an algorithm design. 4. Use and develop data structure based on the current problem requirement.
Synopsis This course introduces the students to data structures and algorithms. The basic concepts in structure, class, array and pointer are discussed in order to understand the fundamental of data structures and algorithms. The course focuses on data structures such as list, stack, queue, tree, searching and hash while sorting, graph and heaps topics cover the algorithms. This also includes the algorithm efficiency for run time. Pseudo code and C++ programming language will be used in algorithm implementation. Apart from the theory, the students must apply the data structures and algorithms in the development of small scale application as a group work. References 1.
2. 3. 4.
5.
Richard F. Gilberg, Behrouz A. Fourouzan, “Data Structures A Pseudocode Approach with C++”, Brooks/Cole Thomson Learning, 2001 Malik, D. S. “Data Structures Using C++”. Thomson Course Technolgy, 2005. Michael Main, Walter Savich, “Data Structures & Other Objects Using C++”, Addison Wesley, 2004. Sartaj, Sahni, “Data Structures, Algorithms and Applications in C++”, Mc Graw Hill International Editions, 1998. Berman A., Michael, “Data Structure Via C++ Objects by Evolution “, Oxford, 1997.
BITP 1213
System Development (3,2,2)
Learning Outcomes At the end of the lesson, students should be able to: 1. Identify and explain all the phases in system development. 2. Follow suitable methodology used in system or application development. 3. Apply system development life cycle based on the current problems. Synopsis This course introduces the students to the basic system development concept, analysis, design, modeling,
methodology, technique, tool and other perspectives that are important to be considered in the development of information system. References 1. Valacich, J. S., George, J. F. & Hoffer, J.A. 2006. Modern th Systems Analysis and Design, 5 Ed, Pearson Prentic Hall. 2. Whitten, J., Bentley L. & Dittman, K. 2001. Systems Analysis and Design Methods, McGraw-Hill. 3. Masrek, M. N., Abdul Rahman, S. & Abdul Jalil, K. 2001. Analisis & Rekabentuk Sistem Maklumat. McGraw-Hill. 4. Kendall, K. E. & Kendall, J. E. 2002. System Analysis and Design. Prentice Hall. 5. Shelly, G., Cashman, T. & Rosenblatt, H. 2000. Systems Analysis and Design, Shelly Cashman Series. 6. Blair, R., Crossland, J., Reynolds, M., Willis, T. nd 2003. Beginning VB.Net, 2 edition, Wiley Productions. 7. Bradley, J. C. & Millspaugh, A. C. 2005. Programming in Visual Basic.Net: Visual Basic.NET 2003 Update Edition, McGraw-Hill International Edition
BITP 1323 Database (3,2,2) Learning Outcomes At the end of the lesson, students should be able to: 1. Identify and explain the concept of database, data modeling (relationship) and SQL statements. 2. Produce data conceptual representation using Entity Relationship Model. 3. Develop database application based on the current problem requirement. Synopsis This course is an introduction to database and file management system. It assists the students to form an understanding of data modeling, file management and database system functionality in information system. The students will be introduced to the process of designing, developing and executing database applications. This course focuses on practical skills to create, control and execute
statement for database relationship. Exercises based on various resources will be given in all lab sessions. The students will submit their exercises at the end of the lab session. The students must present their database application project to demonstrate their understanding of the course. This allows the students to apply their knowledge and the techniques that they have learnt into the real world database applications.
2.
3.
4. 5.
Rob, P. & Coronel, C. (2004) Database Systems: Design, Implementation, and Management 6th Edition. Course Technology. Connolly, T., Begg, C. & Strachan, A. (2005) Database Systems: A Practical Approach to Design, Implementation, and Management. 4th Edition. Addison- Wesley. Hoffer, Jeffrey A ., Prescott, Mary B. & McFadden, Fred R. (2004) Modern Database Management 7th Edition. Prentice Hall Pratt, P.J. (2004) A Guide to SQL Seventh Edition. Course Technology Mannino, M.V. (2001) Database Application Development & Design. McGraw-Hill.
BITP 2213
Software Engineeering (3,2,2)
Learning Outcomes At the end of the lesson, students should be able to: 1. Explain the concept and importance of requirement engineering in software development process. 2. Implement software requirement phase and analyze the requirement engineering specification. 3. Create official documents for software requirement specification based on the current problems by following the software requirement engineering process. 4. Choose a suitable tool to design a case study. Synopsis
References 1.
References 1.
This course introduces the students to system development and software engineering. The topics includes the software lifecycle, requirement analysis, software design, processes in software design, design quality, strategy in design and metric in software testing. This course also covers software project management including the budgeting and quality management.
2. 3. 4. 5.
6.
Sommerville, I (2007) Perisian Engineering, 8th Edition, Addison Wesley. Pressman, R.S (2005) Perisian Engineering A Practitioner’s Approach, 6th Edition. McGraw-Hill. Pfleegar, S.L (2001) Perisian Engineering Theory & Practice. 2nd Edition. Prentice Hall. Braude J.E, (2001) Perisian Engineering: An ObjectOriented Perspective, Wiley. Ghezzi C, Jazayeri M, Mandrioli D, (2003) Fundamentals of Perisian Engineering. 2nd Edition Prentice Hall. Bern Oestereich,(2002), Developing Perisian with UML Object oriented Analysis and Design Practice,. 2nd Edition. Addison-Wesley.
BITS 1123 Computer Organization and Architecture (3,2,1) Learning Outcomes At the end of the lesson, students should be able to: 1. Define and explain computer architecture and organization concept including functional components and their characteristics, performance and the detailed interactions in computer system including system bus, different types of memory and input/output as well as CPU. 2. Apply computer architecture theory to solve the basic functional computer problem. 3. Show and assemble basic computer components. Synopsis This course provides detail of computer system’s functional components, their characteristics, performance and
interactions including system bus, different types of memory and input/output and CPU, as well as practical implementations of the components. This course also covers the architectural issues such as instruction set program and data types. On top that, the students are also introduced to the increasingly important area of parallel organization. References 1.
3.
4. 5.
BITS 1213
Operating System (3,2,2)
Learning Outcomes At the end of the course, students should be able to: 1. 2. 3.
1.
2. 3.
William Stallings, (2007). Computer Organization & th Architecture, 7 Edition. Prentice Hall. Carl Hamacher, Zvonko Vranesic, Safwat Zaky, th (2002). Computer Organization, 5 Ed. McGraw Hill. Irv Englander, (2003). The Architecture of Computer Hardware and System Software: An Information rd Technology Approach., 3 Ed. John Wiley & Sons. James L. Antonakos, (2004). The 68000 th Microprocessor, 5 Edition. Prentice Hall. H.Aslinda, R. Marliza, Computer Organization and Architecture, First Edition.
2.
References
Explain the major components of an operating system. Elaborate the major operating system responsibilities or aspects. Explain the differences of the functionality among various kinds of operating system.
Synopsis This course gives exposure to the students about the basic of operating system which comprises process, memory management, file and I/O and also CPU scheduling. The introduction part covers the evolution of operating system followed by the basic concepts, technology and theories used in operating system such as concurrency, kernel, deadlock and multithreading.
4. 5.
William Stallings, Operating Systems: Internals and th Design Principles 6 Ed., Prentice Hall International, Inc. Silberschatz, A (2003). Operating System Concept th 6 . Ed., John Wiley and Sons, Inc. Nutt, G. (2002), Operating Systems : A modern nd Perspective 2 .Ed., Eddison Wesley Longman, Inc., ISBN 0-201-74196-2 Jason W. Eckert, M. John Schitka. Linux Guide to Certification. Zurina, Fairuz, Zaki, Ariff (2009), Fedora Core 9: For Beginner and Intermediate, First Edition.
BITS 1313 Data Communication & Networking
[3, 2, 2]
Learning Outcomes At the end of the course, students should be able to: 1. Explain and apply the fundamental concept of data communication and networking. 2. Differentiate types of media, network topologies and network technologies. 3. Practice the best technique in developing network 4. Configure and troubleshoot a basic network. Synopsis This course introduces the fundamental concepts and terminology of data communication and networking, encompassing both technical and managerial aspects. It also provides an understanding about the challenges and opportunities faced by the modern businesses. The topics include: fundamentals of telecommunications, data transmission mechanisms, telecommunication media and technologies, considerations for LAN and WAN implementations, the Internet and intranet applications, emerging telecommunications technologies, and trends in the telecommunications industry. Students will also be able to understand, explain and apply the fundamentals of data communication and networking as well as skills in network applications to troubleshoot and configure a basic computer networks using guided or unguided media.
2. References 3. 1. 2. 3. 4.
5.
Behrouz Forouzan, Data Communications and th Networking, 4 Edition, McGraw-Hill, 2007. Andrew S Tanenbaum, Computer Network, Prentice Hall, 1997. E. Ramos, A. Schoroeder and A. Beheler, Computer Networking Concepts, McMillan, 1996. Azhar, Haniza and Zakiah, Komunikasi Data dan Rangkaian (Modul Pengajaran), Edisi Pertama, 2005. B. Nazrulazhar and H. Erman, Data Communications and Networking: Practical st Approach, 1 Edition, Venton, 2008.
BITS 2513 Internet Technology (3,2,2) Learning Outcomes Upon completing this subject, students should be able to: 1.
2. 3.
Apply the concepts of computer networks, core components of the Internet infrastructure, protocol and services. Show the implementation of client and server application Select the best Internet application according to the current situation.
Synopsis Internet has become a major tool in doing business today. The evolutions of web based knowledge also contribute to this phenomenon. Hence, this course is purposely designed to provide an introduction to Internet technologies. This course covers a wide range of material about the Internet and the major areas of study including basic concepts of client and server, networking, Internet Security and its application. References 1.
Douglas E. Comer (2007). The Internet 4th edition. Pearson Prentice Hall.
4.
5.
Behrouz Forouzan, Data Communications and Networking, 4th Edition, McGraw-Hill, 2007. Fred T. Hofstetter(2005), Internet Technologies at Work, McGraw Hill Technology Education Douglas E. Comer (2004), Computer Networks and Internets with Internet Applications, 4th Edition, Pearson Prentice Hall Preston Gralla (2002). How Internet Works, 6th edition. Que Publishing
BITM 1113
Multimedia System (3,2,2)
Learning Outcomes Upon completing this course, students should be able to: 1. Use several media editing software to create original multimedia content. 2. List down and discuss the software and hardware components used in multimedia system. 3. Demonstrate life long learning by relating and describing the fundamental concept of multimedia systems into other subjects (e.g. Software Engineering, Internet Technology, PSM etc). 4. Apply problem solving skills by identifying several different environments in which multimedia might be used and several different aspects of multimedia that benefit other forms of information presentation. Synopsis This subject prepares the students with the basic concept of multimedia, technology and the importance of multimedia application. It covers the introduction to media, multimedia graphic implementation, 2D/3D graphics and animation, video, audio, authoring, multimedia integration and application development. In lab sessions, the students will be introduced to tools for selected media elements and authoring software for media integration. Students will be trained for practical preparation of still image, simple animation, sound and effectively apply it in a multimedia project. Students will be exposed to teamwork, leadership, problem solving and communcation skills while performing their various tasks and project.
References 1. 2. 3. 4. 5. 6.
7.
References
Norazlin et al. Sistem Multimedia, Venton Publishing, 2007 Todd Perkins. Adobe Flash CS3 Profesional Hanson Training, 2008. Tay Vaughan, Multimedia: Making It Work 7th Edition, McGraw-Hill Osborne Media, 2006. Mark Drew and Ze-Nian Li, Fundamentals of Multimedia 4th Edition, Prentice Hall, 2004. Nigel Chapman, Digital Multimedia, John Wiley and Sons, 2004. Ken Abernethy and Tom Allen, Exploring the Digital Domain: An Introduction to Computing with Multimedia and Networking, Pws Pub Co, 1999 Jamalludin Harun & Zaidatun Tasir, Multimedia: Konsep & Praktis, Venton Publishing, 2006
BITM 2113
Web Servers : Apache
1.
2.
3.
4.
5.
Robert W.Sebesta (2005), Programming The World Wide Web – 3rd Edition, Addison Wesley, ISBN: 0-321-31257-0 Harvey Deitel, Paul Deitel, Andrew Goldberg (2003), Internet & Internet & World Wide Web How to Program - 3rd Edition, Prentice Hall, ISBN: 0131450913 Keith Darlington (2005), Effective Website Development – Tools and Techniques, Addison Wesley, ISBN: 0-321-18472-6 Luke Welling, Laura Thomson (2003), PHP and MySQL Web Development -Third Edition, Sams Publishing, ISBN: 0-672-32672-87 Bai, Ekedahl, Farrell, Gosselin, Zak, Kaparthi (2003), The Web Warrior Guide to Web Programming,Thomson Course Technology, ISBN: 0-619-06458-7
Web Application Development (3,2,2)
Learning Outcomes
BITP 3113
Upon completing this course, students should be able to: 1. Explain the concept and the principle of Internet and WWW based on the latest technologies. 2. Identify and develop important components in Web applications which comprises client site technology, server site technology, database server and Web server. 3. Relate relevant key components in developing Web applications.
Learning Outcomes
Synopsis
Synopsis
The purpose of this course is to provide the students with a comprehensive understanding of the tools and problemsolving techniques related to the development of effective World Wide Web. It emphasizes on four (4) components of Web application development which are: Client Site Technologies: HTML, XHTML, CSS, XML, and JavaScript Server Site Technologies: PHP Database Server: MySQL.
Object Oriented Programming (3,2,2)
At the end of the lesson, students should be able to: 1. Apply object oriented programming concept and methods. 2. Build program that implement programming language syntax and semantic in Java application. 3. Develop object oriented application based on the current case study.
This course introduces the students to the object oriented programming methods by using Java programming language. Student will apply and design the basic object oriented structure, swing, event handling, interface components, exception handling, database, multimedia, networking and threads. Student will also develop a complete Java programs and applications.
2. References 1.
Liang ,Y .Daniel,(2008) , Introduction Java th Programming , 7 Ed.,Prentice Hall. Deitel, H.M . & Deitel ,P.J.,(2006) , Java How to th Ed., Pearson Education Program ,7 International . Bronson ,Gary J.,(2004), Object Oriented Program Development Using Java –Class Centered Approach , Thompson Course Technology . nd Farrel,Joyce , (2003),Java Programming 2 Ed.,Thomson Course Technology. Doke, E.Reed ,Satzinger,John W.& Williams, Susan Rebstock , (2002), Object –Oriented Application Development Using Java. Thomson Course Technology.
2.
3.
4. 5.
3.
4. 5.
Russel, S & Norvig, P. (2003). Artificial Intelligence: A Modern Approach, 2nd. Edition, Prentice Hall. Luger, G. F & Stubblefield, W.A. (2002). Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 4th. Edition, Addison Wesley. Negnevitsky, M., (2002), Artificial Intelligence: A Guide to Intelligent System, Addison Wesley. Dean, T, Allen, J & Aloimonos, Y (1995), Artificial Intelligence Theory and Practice, The Benjamin Cummings.
BITU 2913
Learning Outcomes Upon completing this course, students should be able to: 1. 2.
BITI 1113 Artificial Intelligence (3,2,2) Learning Outcomes Upon completing this subject, students should be able to: 1. 2. 3.
Explain the basic definition of artificial intelligence. Identify the types of artificial intelligence techniques. Use the artificial intelligence techniques in problem solving.
Synopsis Students will be exposed to the basic and branches of Artificial Intelligence (AI) such as various search techniques, knowledge representation and reasoning, inference techniques, learning from experience and planning. This course also covers some applications of AI including game playing, expert systems, machine learning, and natural language processing.
3. 4.
Use the knowledge learnt specifically the programming techniques to develop a project. Identify and solve problems systematically based on the information from various resources. Run and produce a project individually. Present and defend the project output.
Synopsis The aim of Workshop 1 is to provide the students with experience and skills to develop and present an individual project. Students must use the knowledge learnt to solve the problems and think creatively to achieve their projects’ objectives and scopes. Students should be able to apply programming technique in their projects. The systems/applications developed must have logic process flow, robust, consistent, have attractive user interface and are able to detect errors in input/output data. At the final stage of this workshop, the students must present and defend their project. A supervisor will supervise the students for the whole 12 weeks and will evaluate the progress during the implementation and final presentation. This course is also a fundamental course to prepare the students for industrial training.
References References 1.
Coppin, B (2004). Artificial Intelligence Illuminated, Jones and Bartlett.
Workshop I (3,0,9)
1. 2.
3.
4. 5.
Burhanuddin Mohd Aboobaider et. all., Software Development Using Visual Basic.NET BITU 2913. Julia Case Bradley, Anita C.Millspaugh, Programming in Visual Basic .NET, McGraw-Hill, 2005 Edition. Jack Koh, Gourab Sen Gupta, Jesicca Goh, Ronnie Peh, VB.net With Database Access, Prentice Hall, 2002. Dave Grundgeiger, Programming Visual Basic .NET, O’Reilly, 2002. Francesco Balena, Programming Visual Basic .NET, Version 2003, Microsoft Press, 2004.
BITU 3923
Workshop II (3,0,9)
Learning Outcomes Upon completing this course, students should be able to: 1. Analyze and develop a group project. 2. Apply the concept of system design and development in their projects. 3. Identify, analyze and organize the changes made to project scope during the project life cycle. 4. Organize a group project with good manner. 5.
Present and defend the project output.
Synopsis This course allows the students to practice their knowledge and experience gained from the courses taken earlier. This course builds the students understanding about problem solving techniques based on their project scopes. The scope of their projects is based on their programme specializations. This course requires the project to be developed in a team of three to five students. References 1. 2. 3.
Schwalbe, K., (2004). Information Technology Project Management, Thomson. Hughes, B., and Cotterell, M., (2002), Software Project Management, McGraw-Hill. Gonzalez, A. and Dankel, D., (2004). The Engineering of Knowledge-Based Systems (Second Edition), Prentice Hall.
4. 5.
Alpaydin, E., (2004). Introduction to Machine Learning, The MIT Press. Russel, S and Norvig, P., (2003). Artificial Intelligence: A Modern Approach (Second Edition), Prentice Hall.
BITU 3926
Industrial Training (6,0,6)
Learning Outcomes Upon completing this course, students should be able to: 1. Be responsible in performing tasks as an ICT worker. 2. Apply skills and knowledge learnt in classes. 3. practice discipline and ethique in performing daily tasks. 4. Use the latest technology in the ICT domains. 5. Interact and communicate with collleagues in a good manner. Synopsis During this course, students will be able to practice the knowledge that they have learnt in UTeM such as analyzing and designing, database programming, data structure and algorithm, operating system, web programming, network and data communiation etc. It is an opportunity for the students to gain ICT knowledge as in the industry. The students can develop soft skills and professionalism through interaction and communication with colleagues. References Industrial Training Committee ”Industrial Training Guidelines”, UNIC, Universiti Teknikal Malaysia Melaka.
BITU 3946 Industrial Training Report (6,0,6) Learning Outcomes Upon completing this course, students should be able to: 1. 2.
Apply the skills and knowledge learnt Use the latest technlogy in the ICT domain.
3.
Organize information to produce a formal report.
1.
Synopsis 2. This course requires the students to produce a report while undergoing the industrial training. The students should be able to apply the courses that they have learnt at UTeM such as to analyze and design, database programming, data structure and algorithm, operating system, web programming, network and data communication etc. It is an opportunity for them to gain industrial ICT knowledge.
3.
Bachelor Degree Project and Diploma Project Committee, PSM Report Guideline, FTMK, Universiti Teknikal Malaysia Melaka. Bachelor Degree Project and Diploma Project Committee, PSM Report Guideline Book, FTMK, Universiti Teknikal Malaysia Melaka . Bachelor Degree Project and Diploma Project Committee, PSM Report Guideline Reference, FTMK, Universiti Teknikal Malaysia Melaka.
BITU 3983
Project II [3,0,9]
References Learning Outcomes Industrial Training Committee ”Industrial Training Guidelines”, UNIC, Universiti Teknikal Malaysia Melaka.
Upon completing this subject, students should be able to: 1.
BITU 3973
Project I [3,0,9]
Learning Outcomes Upon completing this course, students should be able to: 1. 2. 3. 4. 5.
Run testing and validate their systems based on the projects’ timeline. Solve problems related to the industrial need in the ICT domain. Complete the project output that has the commercial value. Present and defend the output. Organize information to produce a formal report.
Synopsis This course joins together all the subjects learnt from year one of the studies including to analyze and to design a specific system, the application of database, algorithm and data structure, web programming, data communication etc. It is compulsory to the final year students to develop a Final Project and to attend the offered courses. References
2. 3. 4. 5.
Run testing and validate their system based on the project timeline. Solve problems related to the industrial need in the ICT domain. Complete the project output that has the commercial value. Present and defend the output. Organize information to produce a formal report.
Synopsis This course joins together all the subjects learnt from year one of the studies including to analyze and to design a specific system, the application of database, algorithm and data structure, web programming, data communication etc. It is compulsory to the final year students to develop a Final Project and to attend the offered courses. References 1.
2.
3.
Bachelor Degree Project and Diploma Project Committee, PSM Report Guideline, FTMK, Universiti Teknikal Malaysia Melaka. Bachelor Degree Project and Diploma Project Committee, PSM Report Guideline Book, FTMK, Universiti Teknikal Malaysia Melaka . Bachelor Degree Project and Diploma Project Committee, PSM Report Guideline Reference, FTMK, Universiti Teknikal Malaysia Melaka.
COURSE CORE SUBJECTS
BITI 2213 - Knowledge Based System (3,2,2) Learning Outcomes
BITI 2113 - Logic Programming (3,2,2) Learning Outcomes Upon completing this subject, students should be able to: 1. Identify the elements and concepts of logic and procedural programming. 2. Produce the Prolog algorithm for solving logic programming problems. 3. Design and implement basic program using logic programming structures. Synopsis Students are exposed to the basic concepts of logic programming such as Prolog syntax and semantic including predicate logic, facts, rules, query, recursive rules, backtracking control, input output and unification. This course is also aimed to prepare the students who will take other Artificial Intelligence subjects. References 1. 2. 3.
4.
5.
Bratko, Ivan, (2001). Prolog: Programming for Artificial Intelligence, 3rd. Edition, Addison Wesley. Mellish, C.S & Clocksin W.F(2003), Programming in Prolog, Springler Verlag. Luger, G. F & Stubblefield, W.A. (2002). Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 4th. Edition, Addison Wesley. Mellish, C.S. and Clocksin, W.F.. (2003). Programming in PROLOG: Using the ISO Standard. 5th Edition. Springer-Verlag Berlin and Heidelberg GmbH & Co. Bramer, M. (2005). Logic Programming with Prolog. Springer-Verlag London Ltd. ISBN: 1852339381.
Upon completing this subject, students should be able to: 1. Explain by relating and describing the fundamental concept of knowledge based system and their components. 2. Assess and identify appropriate concept and components in knowledge based system problem solving. 3. Develop a basic knowledge based system based on appropriate concept and component. Synopsis This course involves introduction to knowledge based system, phases in developing the system, types of knowledge representations, knowledge acquisitions and types of inference techniques and reasoning. Besides, students are exposed to Expert Systems as one of the knowledge based system. References 1.
2.
3.
4. 5.
Gonzalez and D. Dankel (2004). The Engineering of Knowledge-Based Systems (2nd Edition), Prentice Hall. J. Giarratano and G. Riley (2004). Expert SystemsPrinciples and Programming (4th Edition), Thomson/PWS Publishing Company. Efraim Turban & Jay E. Aronson (2005), Decision support systems and intelligent systems, Prentice Hall. Negnevitsky, M., (2002), Artificial Intelligence: A Guide to Intelligent System, Addison Wesley. Russel, S & Norvig, P. (2003). Artificial Intelligence: A Modern Approach, 2nd. Edition, Prentice Hall.
BITI 2223 - Machine Learning (3, 2, 2)
1.
Learning Outcomes
2.
Upon completing this subject, students should be able to: 1. Explain by relating the fundamental concept of machine learning theory. 2. Assess and identify the appropriate techniques in machine learning problem solving. 3. Demonstrate machine learning algorithm based on machine learning concepts.
3.
Synopsis The course aims to provide exposure on the foundation of machine learning, which is the study of how to build a computer system that learns from experience. The course starts with an overview of Data Mining for a background study. Main topics that will be covered are such as concept learning, decision tree learning, Bayesian learning, instance-based learning, learning sets of rules, and reinforcement learning. Besides, some applications of machine learning including robotic control, autonomous navigation, bioinformatics, speech recognition, and web data processing will be introduced.
Explain and analyse the fundamental concept of fuzzy logic. Investigate and identify the appropriate techniques in fuzzy logic problem solving. Manipulate computer programme based on fundamental techniques of fuzzy logic for problem solving.
Synopsis The course aims to provide exposure on the foundation of fuzzy logic as one of the soft computing techniques. The course starts with an overview on the concept of fuzziness. The main topics will cover the algebra, quantities and the logical aspect of fuzzy sets, fuzzy operations, fuzzification, defuzzification and fuzzy control. Various applications of fuzzy control such as the rule-based system, PI type, supervisory and adaptive controllers will be included in the discussion. References 1. 2. 3.
References 4. 1. 2.
3. 4. 5.
Mitchell, T.M., (1997), Machine Learning, McGraw Hill. Witten, I.A., Frank, E., (2005), Data Mining: Practical Machine Learning and Techniques (Second Edition),Morgan Kaufmann. E.N Richard (2003), Learning Bayesian Networks (Hardcover), Prentice Hall. Alpaydin, E., (2004), Introduction to Machine Learning, The MIT Press. Han, J. and Kambel, M. (2000), Data Mining: Concepts and Techniques. Morgan Kaufman.
BITI 3123 - Fuzzy Logic (3,2,2) Learning Outcomes Upon completing this subject, students should be able to:
5.
Nguyen, H. T., Walker, E. A. (1999). A First Course nd in Fuzzy Logic. 2 Edition, CRC Press. Ross, T. J. (2004). Fuzzy Logic with Engineering nd Applications, 2 Edition, John Wiley. Chen, G., Pham, Trung Tat (2000). Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control System. CRC Pr I llc. James, J.B. (2002). An introduction to fuzzy logic and fuzzy sets. CRC Press. McNeill, Martin, Ellen. T. (1994). Fuzzy Logic: A Practical Approach, Academic Press Professional.
BITI 3133 - Neural Networks (3,2,2) Learning Outcomes Upon completing this subject, students should be able to: 1. Explain and analyze the fundamental concept of neural network. 2. Investigate and identify the appropriate techniques in neural network problem solving. 3. Manipulate computer programme based on fundamental techniques of neural network for problem solving.
Synopsis
3.
This course introduces a soft computing technique that is neural network. . Few fundamental theories in neural network will be introduced including biological neural network, artificial statistic neural network, Hebbian learning and rivalry strength learning. Besides, brief introduction to information theory, application and practice of neural network in relevant domains will be discussed. References 1. 2.
3. 4.
5.
6.
7. 8.
Andries, E (2002). Computational Intelligence. An Introduction, John Wiley & Sons. Zilouchian, Jamshidi. (2001). Intelligent Control Systems Using Soft Computing Methodologies. CRC Press, Inc. Kumar, S. (2004). Neural networks : a classroom approach. Mc Graw Hill, New Delhi. Perlovsky, L.I (2001). Neural networks and intellect : using model - based concepts. Oxford University Press, New York. Smith, K. A. (2002) Neural networks in business : techniques and applications. Idea Group Publications. Hershey, P.A. Haykin, S. (1999). Neural Networks. A Comprehensive Foundation. Prentice Hall, New Jersey. Fausett, L. (1994). Fundamentals of Neural Networks. Prentice Hall. Bose, N. K and Liang, P. (1996). Neural Network Fundamentals with Graphs, Algorithms, and Applications. McGraw-Hill.
Synopsis This course covers the underlying theory of agents, the common agent architectures, methods of cooperation and communication, and the potential applications for agents. Students will be exposed to the concept of intelligent agent and multi agent systems. Students will also construct their own agents for solving different types of problems. The potential applications of agents are numerous including web search assistants, travel advisors, electronic secretaries, bidders in on-line auctions, tutoring systems, and actors in games or simulations. Some of the tools to be used are J2SE and Zeus agent building toolkit. References 1.
2.
3.
4.
5. BITI 3113
Intelligent Agents (3,2,2)
Manipulate computer programme based on fundamental techniques of intelligent agents for problem solving.
Michael Wooldridge (2002). An Introduction to MultiAgent Systems. Chichester: England, John Wiley and Sons. Gerhard Weiss (2000). Multiagent Systems: A modern approach to Distributed Artificial Intelligence. The MIT Press. Jacques Ferber (1999). Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley Professional. Joseph P. Bigus & Jennifer Bigus (2001). Constructing Intelligent Agents Using Java: Professional Developer's Guide, 2nd Edition. Wiley. Lin Padgham & Michael Winikoff (2004). Developing IntelligentAgent Systems: A Practical Guide. John Wiley & Sons.
Learning Outcomes Upon completing this subject, students should be able to: 1. Explain and analyze the fundamental concept of intelligent agents. 2. Investigate and identify the appropriate techniques in intelligent agents problem solving.
BITI 3143 - Evolutionary Computing (3,2,2) Learning Outcomes Upon completing this subject, students should be able to: 1. Explain and analyse the fundamental concept of intelligent agents.
2. 3.
Investigate and identify the appropriate techniques in intelligent agents problem solving. Manipulate computer programme based on fundamental techniques of intelligent agents for problem solving.
3. 4.
Synopsis This course introduce problem-solving technique in evolutionary computing. Evolutionary computing uses algorithms which are inspired by mechanisms of biological evolution. These search-algorithms apply the concepts of genetic recombination, mutation, and natural selection in producing the potential solutions. A number of evolutionary computing techniques will be taught, and this course puts greater emphasis on Genetic Algorithms. Others techniques such as Simulated Annealing, Ant Colony Optimization and Memetic Algorithm are also covered in this course.
mechanism in computer software, operating system, database, network system and computer security management. Produce the appropriate security system mechanism for computer software and computer network. Analyze issues that are related to the law and ethics in computer security as well as identify the cyber law associated with computer security issues.
Synopsis Security in Information Technology is a very important issue. It is an area that deserves study by computer professionals, students, and even many computer users. Through this course, student will learn how to control failures of confidentiality, integrity and availability in applications, databases, operating systems and networks alike. Student will also learn on how to plan the recovery solution if any disaster happens to the computing environment.
References References 1. 2. 3.
4.
5.
Haupt, R.L, Haupt, S.E, (2004) Practical Genetic Algorithms, Wiley-Interscience Eiben, A.E., Smith, J.E., (2003) Introduction to Evolutionary Computing, Springer B. Eric, D. Marco, T. Guy, (2001), Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press. Mitchell, M. (1998) An Introduction to Genetic Algorithms (Complex Adaptive Systems), The MIT Press. Drechsler, R. (EDT), Drechsler, N. (EDT) (2002) Evolutionary Algorithms for Embedded System Design (Genetic Algorithms and Evolutionary Computation), Kluwer Academic
BITS 3423
Information Technology Security (3,2,2)
Learning Outcomes At the end of the course, students should be able to: 1. Explain and elaborate the concept of computer security theories and related items. 2. Study and identify the concept and the suitable components in providing service and security
1.
2.
3. 4. 5.
1. Siti Rahayu, Robiah, Mohd Faizal and Nazrulazhar (2006), Information Technology Security, Pearson. W. Stallings (2003). Network Security Essentials: nd Applications and Standards, 2 edition, Prentice Hall, Inc. C.P. Pfleeger, S. L. Pfleeger (2003). Security in rd computing 3 Ed., Prentice Hall International, Inc. nd D. Gollmann (2005). 2 Edition, Computer Security, John Wiley & Sons, Inc. B. Schneier (1996). Applied Cryptography: nd Protocols, Algorithms and Source Code in C 2 Ed, John Wiley & Sons, Inc.
ELECTIVE SUBJECTS
BITI 3513 - Artificial Intelligence in Manufacturing (3,2,2)
BITI 3213 – Decision Support System (3,2,2)
Learning Outcomes
Learning Outcomes Upon completing this subject, students should be able to: 1. Explain and analyse the fundamental concept of decision support system. 2. Investigate and identify the appropriate techniques in decision support system problem solving. 3. Manipulate computer programme based on fundamental techniques of decision support system for building intelligent system.
Upon completing this subject, students should be able to: 1. Analyse the situation of manufacturing operation and how does artificial intelligent technique improve manufacturing operation performance. 2. Research in artificial intelligent techniques which is appropriate for producing intelligent manufacturing environment. 3. Manipulate computer programme based on fundamental techniques of artificial intelligence in manufacturing for problem solving.
Synopsis
Sinopsis
The course aims to provide students with knowledge of various decision support systems and artificial intelligence systems and the ways in which they support effective decision making in organisations. Topics covered are introduction to DSS, decision makers, types of DSS, development of DSS, modelling and optimization, group DSS, executive ESS, and intelligent DSS.
References 1.
2.
3.
4. 5. 6.
Cylde W. Holsapple dan Andrew B. Whinston. (1996), Decision Support Systems: A KnowledgeBased Approach, Singapore: International Thomson Publishing (ITP) ISBN 0314065105 Efraim Turban & Jay E. Aronson (2005), Decision Support Systems and Intelligent Systems, Prentice Hall. ISBN 0130894656 George M. Marakas (2003), Decision Support Systems in the 21st Century, Prentice Hall. ISBN 013122848X Matthew Liberatore, Robert Nydick (2002) Decision Technology: Modeling, Software, and Applications ISBN 0471417122 Srinivasan, Ananth.(2000) Implementing DecisionSupport Systems : Methods, Techniques, and Tools McGraw-Hill, ISBN 0077095081
Students are exposed to manufacturing operations in several areas/domain such as system design, planning, scheduling, monitoring and control. The theory and principles accompanied by the real world problem in each area will be studied. It will then be extended with the applications of AI techniques such as knowledge-based system, Neural Network and other that the students already learn from previous AI subjects. At the end of the course, students will involve in the development of intelligence manufacturing module system by using appropriate artificial intelligence techniques. References 1. 2.
3. 4. 5.
Kusiak, A., (2000), Computational Intelligence in Design and Manufacturing, John Wiley & Sons Wang, A., Kusiak, A.(2000) Computational Intelligence in Design and Manufacturing Handbook. CRC Press Rusell, S. & Norvig, P (2003), Artificial Intelligence a Modern Approach 2 ed. Prentice Hall. Poole D., Mackworth A., & Goebel, R. (1998) Computational Intelligence. Oxford University Press. Bourbakis, N.G., (1998), Artificial Intelligence and Automation, World Scientific
BITI 3523
Artificial Intelligence in Robotics and Automation (3,2,2)
BITI 3413 - Natural Language Processing (3,2,2) Learning Outcomes
Learning Outcomes Upon completing this subject, students should be able to: 1. Explain and analyze fundamental concepts related to robotic such as direct kinematic and inverse kinematic of manipulators. 2. Research in programming techniques of robot manipulator’s dynamic equations. 3. Model and stimulate robotic programming for human function simulation.
Upon completing this subject, students should be able to: 1. Explain and analyze the fundamental concept of natural language processing. 2. Investigate and identify the appropriate techniques in natural language processing problem solving. 3. Manipulate computer programme based on fundamental techniques of natural language processing for building intelligent system. Synopsis
Synopsis This course covers introduction of robotics, which includes mechanical structure of robot systems, mechanics of robot manipulators and control systems. The students will be exposed to the fundamental of automation and robotic programming. References 1.
2. 3.
4.
5. 6.
John, J.C. (2005), Introduction to Robotics Mechanics and Control. Prentice Hall, Pearson Education, Inc. Predco, M. (2003), Programming Robot Controllers. McGraw-Hill. Law, K.H. (2002), Robotics Principles and System Modeling. Prentice Hall, Pearson Education Asia Pte. Ltd. Joseph, L.J. (2003), Robot Programming: A Practical Guide to Behavior-Based Robotics. McGrawHill. Jones, J. and Roth, D. (2004), Robot Programming. McGraw-Hill. Dusko, K., Miomir, V. (2003), Intelligent control of robotic systems. Kluwer academic Publisher.
This course provides knowledge to students about natural language processing (NLP). Topics covered: English grammar, grammar representations, NLP tasks including syntactic analysis (grammars and parsing), semantic analysis (word and sentence meaning), and discourse analysis (pronoun resolution and text structure) and its applications such as machine translation, information retrieval and search, information filtering and text categorization, information extraction, spell checking, dictation, command interfaces, question-answering systems, and other dialog systems. References 1.
Allen, J. (1995). Natural Language Understanding. Benjamin/Cummins Publishing.
2.
Jurafsky, D. & Martin, J. (2000). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech. Prentice-Hall. Manning, C. D. and Schütze, (1999). H. Foundations of Statistical Natural Language Processing. The MIT Press. Gal, A., Lapalme, G., Saint-Dizier, P., & Somers, H. (1991). Prolog for Natural Language Processing. John Wiley & Sons. Rusell, S. & Norvig, P (2003), Artificial Intelligence A Modern Approach, 2nd edition. Prentice Hall.
3.
4.
5.
BITI 3313
Image Processing and Pattern Recognition (3,2,2)
BITM 3133
Computer Games Development (3,2,2)
Learning Outcomes Learning Outcomes At the end of the course, students should be able to: Upon completing this subject, students should be able to: 1. 1. 2.
3.
Explain and and analyse the fundamental concept of image processing and pattern recognition. Investigate and identify the appropriate techniques in image processing and pattern recognition problem solving. Manipulate computer programme based on fundamental techniques of image processing and pattern recognition for building intelligent system.
Synopsis This course provides basic image processing techniques to students, such as sampling, digitization, preprocessing, segmentation, feature extraction, and transformation. The course emphasize on object recognition of computer vision systems. The students will also be exposed in pattern recognition and their application in other fields such as robotics, medical and remote sensing.
References 1. 2.
3.
4.
Gonzalez, R. C. & Woods, R. E. (2007) Digital Image Processing, 3nd Edition, Prentice Hall. Gonzalez, R. C. & Woods, R. E. & Eddins, S. L. (2004) Digital Image Processing Using MATLAB, Prentice Hall. McAndrew, A. (2004) An Introduction to Digital Image Processing with MATLAB, Course Technology. Shapiro, L.G. & Stockman, G.C. (2001) Computer Vision, Prentice Hall.
2.
3. 4.
Explain and report the principles, basic of interface design and technologies behind the rules to play the games. Show how the functions of computer games can be used to create experience, including rules design, game mechanic, game balancing, social game integration and the integration of visual, audio, tactile and textual elements into the game experience. Describe and construct how characters, plots and dialogues are developed in interactive story telling. Construct text based and graphical computer games’ prototypes.
Synopsis Electronic game is one of the most popular forms of entertainment that we need to understand from the perspectives of commercial products, cultural phenomena and computer technology particularly computer graphics. An understanding of software technologies such as graphics, networks, software design and artificial intelligence as well as the cultural context is necessary in designing and developing computer games. This subject focuses on the design of computer games and how different technologies can be adopted in practical projects.
References 1. 2. 3. 4. 5.
Gary R (2007), ActionScript 3.0 Game Programming University. Breackeen D., Barker B. & Vanheluwe (2004) Developing Games In Java, New Riders. Crawford C.(2003) Chris Crawford on Game Design. Prentice Hall. Crawford C. (2003) The Art of Interactive Design. No Strach Press. Rollings A & Adams E. (2003) Game Architecture and Design. New Riders.
