50 BIM 33603 Special Topics in Multimedia Computing Prerequisite Course(s): None Synopsis This course focuses on research in technology transfer and new findings in multimedia computing. Additionally, this course requires students to have direct involvement in the discussion either in the classroom or newsgroup/online forum. Topics include current issues and challenges in multimedia computing. References 1. Li, Q. & Shih, T. K., (2010). Ubiquitous multimedia computing. Boca Raton, FL: CRC Press. Call Number: QA76.575 .U34 2010. 2. Halvadar, P. & Medioni, G. (2009). Multimedia Systems: Algorithms, Standards, and Industry Practices (Advanced Topics). Boston, MA: Cengage Learning 3. Sasaki, H. ed., 2008. Intellectual property protection for multimedia information technology. Hershey, PA: Information Science Reference. Call Number: QA76.575 .S27 2008. 4. Zhang, Y., 2008. Broadband mobile multimedia: techniques and applications. Boca Raton, FL: Taylor & Francis. Call Number: QA76.59 .B76 2008. 5. Flammini, F. (2013). Effective Surveillance for homeland security: balancing technology and social issues. Call Number: TK7882 .E2 .E43 2013 6. Leung, A. (2014). Multimedia, Communication and Computing Application. Proceedings of 2014 International Conference on Multimedia, Communication and Computing Application (MCCA2014), Xiamen, China. Oct 16-17, 2014. Call Number: TK5105.15 .M88 2015 7. Roesler, V., Barrere, E. and Willrich, R. (2020). Special Topics in Multimedia, IoT and Web Technologies. Switzerland: Springer BIM 33703 Digital Video Technology Prerequisite Course(s): Taken BIM10103 Fundamentals of Multimedia Computing Synopsis This course focuses on discussion of digital video technology and processing. Students will learn topics on introduction to video, digital video representation, video production, data processing, digital signal processing, audio and video processing, and video standards. References 1. Zettl, H., 2010. Video basics. 6th ed. Boston, MA: Wadsworth. Call Number: TR882.5 .Z47 2010. 2. Paul, S., 2011. Digital video distribution in broadband, television, mobile and converged networks: trends, challenges and solutions. Chichester : John Wiley. Call Number: TK5105.15 .P38 2011. 3. Schaefermeyer, S., 2007. Digital video basics. Hoboken, NJ : Wiley. Call Number: TR860 .S32 2007 4. Weise, M. & Weynand, D., 2007. How video works: from analog to high definition. 2nd ed. Burlington, MA: Focal Press. Call Number: TK9961 .W44 2007. 5. Yue L.W., 2013. Digital Media Primer: Digital Audio, Video, Imaging, and Multimedia Programming. 2nd ed. Shandar J.: Pearrson.
51 BIM 33803 Digital Audio Technology Prerequisite Course(s): Taken BIM10103 Fundamentals of Multimedia Computing Synopsis This course focuses on discussion of digital audio technology and processing. Students will learn topics on introduction to audio, digital audio representation, audio production, data processing, digital signal processing, audio and video processing, and video standards. References 1. Woodhall, W. (2011). Audio production and postproduction. Sudbury, MA: Jones and Bartlett Learning. Call Number : TK7881.4 .W66 2011 2. Lerch, A. (2012). An introduction to audio content analysis : applications in signal processig and music informatics. Hoboken, N.J. : Wiley. Call Number TK7881.4 .L47 2012 3. Bernard, G. (2011). Speech and audio signal processing and perception of speech and music. Oxford: John Wiley. Call Number : TK7882.S65 .G64 2011 4. McLoughlin, I. (2009). Applied speech and audio processing: with Matlab examples. Cambridge ; New York : Cambridge University Press. 5. Spanias, A. (2007). Audio signal processing and coding. Hoboken, NJ : John Wiley. Call Number: TK5102.92 .S72 2007 BIT 34503 Data Science Prerequisite Course(s): None Synopsis This course will cover various topics related to introduction to data, data science methodology, dealing with databases, data gathering, exploratory data analysis (EDA) , text mining, supervised learning, visualizing data, analysis and big data landscape. References 1. Grus, Joel. (2019). Data Science from Scratch: First Principles with Python 2nd Edition. O'Reilly Media. [ISBN-10: 1492041130, ISBN-13: 978-1492041139] 2. Hardoon, David Roi and Ng, Reuben. (2021). The Hitchhiker's Guide to Data Science. Chapman & Hall / CRC Big Data Series. [ISBN-10: 1498724558, ISBN-13: 978-1498724555] 3. Bilokon, Paul A. (2020). Python, Data Science and Machine Learning: From Scratch to Productivity. World Scientific Pub Co Inc. [ISBN-10: 9811215723, ISBN-13: 978-9811215728] 4. Kelleher, John D., and Tierney, Brendan. (2018). Data Science. MIT Press Essential Knowledge Series. [ISBN-10: 0262535432, ISBN-13: 978-0262535434] 5. Blum, Avrim., and Hopcroft, John. (2020). Foundations of Data Science. Cambridge University Press. [ISBN-10: 1108485065, ISBN-13: 978-1108485067] 6. Pierson, Lillian. (2017). Data Science For Dummies, 2nd Edition For Dummies Publisher. [ISBN10: 9781119327639, ISBN13:978-1119327639] BIT 34403 Deep Learning Prerequisite Course(s): None Synopsis This course is an introduction to deep learning, a branch of machine learning concerned with the development and application of modern neural networks. Deep learning algorithms extract layered highlevel representations of data in a way that maximizes performance on a given task. This course will
52 focus on both theory and practice by covering models for various applications, how they are trained and tested, and how they can be deployed in real world applications. References 1. Iba, Hitoshi, and Noman, Nasimul. (2020). Deep Neural Evolution: Deep Learning with Evolutionary Computation (Natural Computing Series) 1st Edition. Springer. [ISBN-10: 9811536848, ISBN-13: 978-9811536847]. 2. Kelleher, John D. (2019). Deep Learning (The MIT Press Essential Knowledge series). MIT Press. [ISBN-10: 0262537559, ISBN-13: 978-0262537551]. 3. Charniak, Eugene. (2019). Introduction to Deep Learning. The MIT Press. [ISBN-10:0262039516, ISBN-13: 978-0262039512]. 4. Aggarwal, Charu C. (2018). Springer. Neural Networks and Deep Learning: A Textbook 1st Edition. [ISBN-10: 3319944622, ISBN-13: 978-3319944623]. 5. Chollet, François. (2017). Deep Learning with Python 1st Edition. Manning Publications. [ISBN-10: 9781617294433, ISBN-13: 978-1617294433]. 6. Goodfellow, Ian., Bengio, Yoshua, Courville, Aaron. (2016). Deep Learning (Adaptive Computation and Machine Learning series). The MIT Press. [ISBN-10: 0262035618, ISBN-13: 978-0262035613]. BIT 34303 Machine Learning Prerequisite Course(s): None Synopsis An introduction to machine learning theories and algorithms. Topics include supervised Learning (artificial neural networks, support vector machines) and unsupervised learning (clustering, dimensionality reduction). References 1. Lee Meng Wei. (2019). Python Machine Learning. Wiley. 2. Mohri Mehryar, Afshin Rostamizadeh, and Ameet Talwalkar. (2018). Foundations of Machine Learning. The MIT Press 3. Andreas C. Müller & Sarah Guido. (2016) Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media 4. Shalev-Shwartz S., Ben-David S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press. 5. Christopher M. Bishop. (2011). Pattern Recognition and Machine Learning. Springer BIT 30303 Decision Support System Prerequisite Course(s): None Synopsis This course introduces topics such as Data and Model Management, Decision Making, Decision Making Process, Decision Making Modelling, Decision Support System Design and Development, User Interface Component, Decision Support System Integration and Implementation, Group Decision Support System References 1. Ramesh Sharda, Dursun Delen & Efraim Turban (2019) Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support (11th Edition). Pearson
53 2. S. Christian Albright (2015) VBA for Modelers: Developing Decision Support Systems with Microsoft Office Excel 5th Edition. Cengage Learning 3. Efraim, T., Aronson, J. E. Liang T. & McCarthy R.V. (2011). Decision support and business intelligence systems. 9th ed. New York: Prentice Hall. Call Number: HD30.2 .D42 2007 4. Chiang S.J., (2011). Efficient Decision Support System : Practice and Challenges in Multidisciplinary Domains. Rijeka:INTECH OPEN ACCESS 5. Efraim.T., et.al. (2014). A Business Intelligence and Analytics: Systems for Decision Support. New York : Pearson. 6. Chiu, C. M., Liang, T. P., & Turban, E. (2014). What can crowdsourcing do for decision support?. Decision Support Systems, 65, 40-49. BIT 33603 Data Mining Prerequisite Course(s): None Synopsis This course provides detailed explanations on data mining and machine learning, which include: classification, clustering, association rules and so on. Emphasis will be laid on performance and implementation issues, as well as on applications such as web mining. References 1. Galit Shmueli, Peter C. Bruce, Peter Gedeck & Nitin R. Patel (2019). Data Mining for Business Analytics: Concepts, Techniques and Applications in Python. Wiley 2. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. 3. Larose, D.T. & Larose, C.D. (2015). Data mining and predictive analytics. Hoboken, NJ: John Wiley. Call Number: QA76.9.D343 .L375 2015. 4. Han, J., Kamber, M., & Pei, J. (2012). Data mining: concepts and techniques. Burlington, MA: Elsevier. Call Number: QA76.9.D343 .H36 2012. 5. Gupta, G.K. (2011). Introduction to data mining with case studies. New Delhi: Prentice-Hall. Call Number: QA76.9.D343 .G86 2011. 6. Kudyba, S. (2014). Big data, mining, and analytics: components of strategic decision making. CRC Press. BIT 20903 Artificial Intelligent Prerequisite Course(s): None Synopsis This course introduces topics such as searching and problem solving, knowledge representation, logic, knowledge engineering, machine learning, and artificial intelligence future. References 1. Russel, S., and Norvig, P., (2020). Artificial Intelligence: A Modern Approach. 4th Edition. Pearson Education. [ISBN-13: 978-0134610993, ISBN-10: 0134610997]. 2. Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux Publications. [ISBN-10:0374257833, ISBN-13: 978-0374257835] 3. Stone, J. V., (2019). Artificial Intelligence Engines. Sebtel Press. [ISBN-13: 978-0956372819, ISBN-10: 0956372813]. 4. Mehrotra, D., (2019). Basics of Artificial Intelligence & Machine Learning. Notion Press. [ISBN-10: 1645872823, ISBN-13: 978-1645872825].
54 5. Wilkins, N., (2019). Artificial Intelligence. Bravex Publications. [ISBN-10: 1950922510, ISBN-13: 978-1950922512]. 6. Rothman, D., (2018). Artificial Intelligence by Example. Packt Publishing. [ISBN-10: 1788990544, ISBN-13: 978-1788990547]. 7. Li, D., & Du, Y. (2017). Artificial intelligence with uncertainty. CRC press. UQU 40103 Professional@Works Prerequisite Course(s): None Synopsis The Professional at Work course is designed to improve the ability of students to develop their technical skills in professionalism, social responsibility, and environmental sustainability. Nurturing and empowering the student with these skills could enhance the student's professionalism prior to entering the workspace. The philosophy of the course is ongoing, systematic, and aimed toward a fulfilling work life, which is part of their overall plan for personal development. This course includes an introduction to professional practice, ethics, legal, innovation and infrastructure, social responsibility, and professional environment. Also, this course was developed by referring to Sustainable Development Goals (SDG) and Politic, Economy, Social, Technology, Environment, and Legal (PESTEL) guidelines. Particularly, students will propose a suitable community service project that deals with local/community issues that lead to professional practices. References 1. Lydia E. Anderson & Sandra B. Bolt (2016). Professionalism : skills for workplace success. Pearson, c2013 ISBN 9780132624664 2. Department of Economic and Social Affairs, United Nation (2019). Handbook for th preparation of valuntary national reviews 3. Purohit, S. S. (2008). Green technology : an approach for sustainable environment. ISBN: 9788177543438, [S494.5.S86 .P87 2008] 4. Russ, Tom (2010). Sustainability and design ethics. ISBN: 9781439808542 [TA157 .R87 2010] 5. Yoe, Charles (2012). Principles of risk analysis : decision making under uncertainty. ISBN: 9781439857496 [T57.95 .Y63 2012]
55 Synopsis of Free Module Courses BIM 30703 Multimedia Project Management Prerequisite Course(s): None Synopsis This course focuses on the multimedia project management. Topics include context of interactive media project, starting the interactive media project, group management, stakeholders and the influences, partnership approach for developers/customers, problem solving on project development, user contributions, testing and archiving, legal issues, intellectual property and marketing. References 1. Schwalbe, K. (2014). Information Technology Project Management Seventh Edition. Course Technology CENGAGE Learning 2. Project Management Institute, (2013). A Guide to the Project Management Body of Knowledge (PMBOK Guides). 5th edition. Pennsylvania: Project Management Institute. 3. England, E. & Finney, A., (2007). Managing interactive media: project management for web and digital media. 4th ed. Essex: Addison Wesley. Call Number: QA76.575 .E53 2007. 4. Frick, T., (2007). Managing interactive media projects. Clifton Park, NY: Delmar Cengage Learning. Call Number: QA76.76.I59 .F74 2008. 5. Dawson, C. W., (2009). Projects in computing and information systems: a student’s guide. 2nd ed. Essex: Addison Wesley. Call Number: QA76 .D38 2005 6. Hughes, B. & Cotterell, M., (2009). Software project management. 5th ed. Berkshire: McGraw Hill. Call Number: QA76.76 .C67 2002 N1. BIC 21102 Professional Ethics and Occupational Safety Prerequisite Course(s): None Synopsis This course discusses topics related to professional ethics in computing. Topics include introduction to professional ethics in computing, professional ethics and responsibilities, personality in computing ethics, security and control, copyright and intellectual property, freedom of speech, politeness, filtering and pornography, and cyber laws in Malaysia. References 1. Reynolds, G., 2013. Ethics in information technology 5th ed. Boston, MA: Course Technology. Call Number: HC79.I55.R49 2015. 2. Quinn, M. J., 2010. Ethics for the information age. 4th ed. Boston: Addison Wesley Call Number: QA76.9.M65 .Q74 2011. 3. Baase. S., 2008. A gift of fire: social, legal and ethical issues for computer and the Internet. 3 rd ed. Upper Saddle River, NJ: Prentice Hall. 4. MacKinnon, B., 2015. Ethics: theory and contemporary issues 8th ed. California: Wadsworth Publishing. Call Number: BJ1012.M324 2015. 5. Occupational Safety and Health Act and Regulations. MDC Publishers Printer Sdn. Bhd. 2001. Call Number KPG1390.M34 2001 rw. 6. Factories and Machinery Act and Regulations. MDC Publishers Printer Sdn. Bhd. 2001. Call Number: KPG1390.A4 2001 rw. 7. Furaker, B., 2012. Commitment to work and job satisfaction: studies of work orientations. Call Number: HD4905.C65 2012.
56 Career and Further Education Prospect With a Bachelor of Computer Science in Multimedia Computing, the student may pursue various professional careers including: ● Multimedia Content Designer ● Multimedia Content Developer ● Interactive Media Developer ● Multimedia Subject Matter Expert ● Multimedia Instructional Designer ● Multimedia Mobile Application Developer ● Multimedia Programmer ● Multimedia Specialist ● Multimedia Web Designer ● Multimedia Web Developer ● Game Developer ● Game Designer ● Game Programmer ● 2D/3D Animator ● 2D/3D Graphics Designer ● AR/VR Animator/ Designer/ Developer ● Audio-Video Producer ● Audio-Video Specialist ● Multimedia Creative Director ● Storyboard designer and many other Multimedia or Computer Science related jobs. These jobs are said to be the fastest growing occupation in the next decade. Students also can further their study in Master of Computer Science by research, coursework or mix-mode in related higher learning education institution.
57 Further Education Pathway MASTER OF INFORMATION TECHNOLOGY
58 Source: Malaysian Qualification Framework
59 Centre for Academic Development and Training Universiti Tun Hussein Onn Malaysia 86400 Batu Pahat, Johor Darul Ta’zim www.uthm.edu.my