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CSIT

Information Technology

CSIT 5100
Engineering Reliable Object-Oriented Software Systems
[3-0-0:3]
Previous Course Code(s)
CSIT 510
Description
Discussion of the latest enabling technologies used for the engineering of reliable software applications. These technologies include the modeling, design, testing and analysis of software applications.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Apply software engineering principles to develop and manage software systems.
- 2.
  Apply industrial practice and software tools to develop and manage software systems.
CSIT 5110
Multimedia Development
[3-0-0:3]
Previous Course Code(s)
CSIT 511
Description
Multimedia fundamentals and design issues. Audio fundamentals and audio processing. Image fundamentals and image processing. Video fundamentals and video processing. Internet multimedia. Integrated multimedia.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain different types of signals and how they can be controlled.
- 2.
  Create programs for the generation of audio and music.
- 3.
  Explain different colour models and how they may be applied.
- 4.
  Explain various algorithms for the processing of images.
- 5.
  Develop a multimedia project.
CSIT 5130
Building Applications for Computational Finance
[3-0-0:3]
Previous Course Code(s)
CSIT 513, CSIT 600L
Background
Programming experience
Description
Financial arithmetic and financial instruments overview. Introduces tools and technologies for building applications in computational finance. Familiarity with Excel functionalities and VBA programming skills. Advanced Excel C++ add-in development. Object oriented modeling and design.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Apply the time value of money and price fixed income instruments.
- 2.
  Translate financial models into spreadsheets applications.
- 3.
  Use VBA to build user defined functions.
- 4.
  Develop financial functions using Excel C++ add-in.
CSIT 5210
Data Mining and Knowledge Discovery
[3-0-0:3]
Previous Course Code(s)
CSIT 521
Co-list with
MSBD 5002
Exclusion(s)
COMP 5331, DSAA 5002, MSBD 5002, MFIT 5004
Description
Data mining has recently emerged as a major field of research and applications. Aimed at extracting useful and interesting knowledge from large data repositories such as databases and the Web, data mining integrates techniques from the fields of database, statistics and AI.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Apply the clustering techniques to find clusters within the data.
- 2.
  Use the classification techniques to conduct classification and predication.
- 3.
  Use the knowledge of frequent pattern mining to discover patterns from the data.
- 4.
  Conduct mining over social media and text data and detect outliers from the data.
CSIT 5300
Advanced Database Systems
[3-0-0:3]
Previous Course Code(s)
CSIT 530
Description
This advanced database course addresses a number of selected data management issues and introduces emerging database related techniques.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Describe important concepts in database management systems, including conceptual modeling and data models, logical and physical database design, and query languages.
- 2.
  Describe a number of data management issues, including query processing and optimization, transaction management, and concurrency control protocols.
- 3.
  Apply database theories to practical database design and application development.
CSIT 5400
Computer Graphics
[3-0-0:3]
Previous Course Code(s)
CSIT 540
Exclusion(s)
COMP 5411
Description
Introduction to image synthesis and digital modeling. Topics include color theory, image processing, affine and projective geometry, hidden-surface determination, photorealistic image synthesis, advanced curve and surface design, dynamics, realistic character animation.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain algorithms for processing the pixel representation of an image.
- 2.
  Explain the fundamentals of the graphics rendering pipeline.
- 3.
  Use appropriate mathematics at various stages in the graphics pipeline.
- 4.
  Explain and apply techniques, such as light representation, texture mapping and shadow generation, in the generation of realistic graphics output.
- 5.
  Discuss the basic techniques of global illumination.
- 6.
  Develop basic programs on the GPU to generate graphics output.
CSIT 5410
Recognition Systems
[3-0-0:3]
Previous Course Code(s)
CSIT 600M
Description
It is getting easier and more and more common to install or use recognition systems in our daily lives and working environments, such as fingerprint recognition systems, face and iris recognition systems, car plate and vehicle recognition systems, industrial automation inspection systems, medical diagnosis and surgical planning systems, image search systems. This course aims to provide students with a sound background in the area of recognition systems. Tentative topics include various examples of recognition systems, and related techniques in image analysis, computer vision, and pattern recognition.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify concepts and techniques to recognize image features and patterns.
- 2.
  Identify concepts and techniques to recognize different objects, e.g. iris, face, fingerprint, license plate and vehicle.
CSIT 5500
Advanced Algorithms
[3-0-0:3]
Previous Course Code(s)
CSIT 6000E
Description
This course covers some advanced algorithms. Topics include sorting and data structures, divide and conquer, string processing, dynamic programming, graph algorithms, shortest path, maximum flow, stable marriage, and streaming.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Analyze the time and space efficiency of an algorithm as a function of the size of a problem instance.
- 2.
  Apply advanced algorithmic design and analysis techniques to solve problems.
- 3.
  Identify various data structures and apply them in designing algorithms.
- 4.
  Explain the correctness of algorithms.
- 5.
  Describe and apply some advanced algorithms for combinatorial optimization and graph problems.
CSIT 5610
Computer Networks: An Internet Perspective
[3-0-0:3]
Previous Course Code(s)
CSIT 561
Description
This course discusses in-depth the architectures, protocols, and other key issues in the design of the global Internet. Topics include: common Internet applications, layered network architecture, switching techniques, local area networks, routing, transport, and multimedia networking.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Define and describe the basic working principles of computer networks, their architecture and protocols.
- 2.
  Describe the principles of networked applications, including client-server based applications (e.g. HTTP, FTP, SMTP and DNS) and peer-to-peer based applications (e.g. BitTorrent, Skype, etc.).
- 3.
  Discuss transport layer protocols, such as TCP and UDP, and recognize their functions and the services they provide.
- 4.
  Explain congestion in computer networks, its manifestations and its causes, and discuss congestion control mechanisms available in the Internet to tackle it.
- 5.
  Illustrate the principles of network layer services and discuss routing algorithms and their applications in the Internet.
- 6.
  Describe the services of the link layer and identify basic link layer protocols and medium access mechanisms.
CSIT 5710
Cryptography and Security
[3-0-0:3]
Previous Course Code(s)
CSIT 571
Exclusion(s)
COMP 5631
Description
The design and analysis of ciphers, public-key cryptography, digital signature, user and data authentication, nonrepudiation, data integrity, public-key infrastructure, secret sharing, key management, cryptographic protocols, systems security, network security, and Web security.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify and formulate security problems in information technology.
- 2.
  Apply and integrate security principles for solving security problems in information technology.
- 3.
  Communicate effectively through assignment writing and project designing.
CSIT 5800
Introduction to Big Data
[3-0-0:3]
Previous Course Code(s)
CSIT 6000D
Description
This course aims at providing students with the fundamental concepts and overview of Big Data, and engaging in open discussion about the challenges and opportunities that it brought about. Topics to be covered include: Properties of Big Data, Big Data Integration, Big Data Mining, and Technologies and Tools on Big Data Analytics.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain what Big Data is and the impact of Big Data to the society.
- 2.
  Identify the characteristics and the challenges of Big Data.
- 3.
  Describe the steps involved in the process of Big Data analysis, including data collection, data preprocessing, data integration, and data mining.
- 4.
  Apply working knowledge on how to structure the analysis with the steps of Big Data analysis process.
- 5.
  Apply Big Data analytics technologies to discover the information hidden in vast amount of data.
CSIT 5900
Artificial Intelligence
[3-0-0:3]
Previous Course Code(s)
CSIT 6000F
Exclusion(s)
MSBD 5015
Description
This course will cover advanced topics in artificial intelligence including machine learning, agent design, mulitiagent systems, game search, natural language processing and knowledge representation and reasoning systems.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Examine and formulate problems as AI heuristic search problem.
- 2.
  Model and design autonomous agents.
- 3.
  Examine and formulate multiagent problems as games.
- 4.
  Examine and formulate problems as machine learning problem.
- 5.
  Integrate reasoning and machine learning in natural language understanding.
CSIT 5910
Machine Learning
[3-0-0:3]
Previous Course Code(s)
CSIT 6000G
Exclusion(s)
COMP 5212, DSAA 5013, MSBD 5012
Description
This course covers core and recent machine learning algorithms. Topics include supervised learning algorithms (linear and logistic regression, generative models for classification, learning theory), deep learning algorithms (feedforward neural networks, convolutional neural networks, recurrent neural networks), unsupervised learning algorithms (variational autoencoders, generative adversarial networks, mixture models), and reinforcement learning (classic RL, deep RL).
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Describe an overview of Machine Learning as a subject of study.
- 2.
  Identify the fundamental issues in machine learning.
- 3.
  Recognize core and recent machine learning algorithms.
- 4.
  Apply core and recent machine learning algorithms to solve real-world problems.
CSIT 5920
Financial Technology for Engineering Professionals
[3-0-0:3]
Previous Course Code(s)
CSIT 6000H
Exclusion(s)
ISOM 5330, ISOM 5340
Description
Innovative computing technology has significantly changed the financial landscape in banking, insurance, gaming (gambling), investment and their respective regulatory frameworks. This course will give students new insights and solid understanding of how and why different IT infrastructure, technical designs and implementations are detrimental to success, and prepare the students for senior technology leadership positions in the banking, insurance, investment, IT audit, government/regulator, innovation and entrepreneurship areas. The course will cover the challenging FinTech issues in (i) IT strategy and governance, (ii) IT development and operations and (iii) the future data ecosystem. There will be class discussions and presentations on system design, flow analysis, modeling, POC (proof of concept), and MVP (minimal viable product) of various FinTech, lnsurTech and RegTech applications.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Describe FinTech and different business & finance model.
- 2.
  Identify FinTech and different payment systems.
- 3.
  Define digital identity, privacy and authenticity.
- 4.
  Apply BlockChain/DLT to solve real-world problems.
- 5.
  Recognize legal challenges and digital forensics in FinTech.
CSIT 5930
Search Engines and Applications
[3-0-0:3]
Previous Course Code(s)
CSIT 6000I
Description
Information retrieval techniques; document indexing, searching and ranking; search methods for web data, personalization, learning to rank; applications.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify the history, evolution, impacts and challenges of web-scale search engines.
- 2.
  Define information retrieval models, document indexing, searching and ranking.
- 3.
  Evaluate the performance of search algorithms using performance metrics.
- 4.
  Develop programs to implement search engine functions and components.
- 5.
  Design methods to apply machine learning to solve search problems.
- 6.
  Apply search engine concepts to recommendation and summarization problems.
CSIT 6000
Topics in Information Technology
[1-3 credit(s)]
Previous Course Code(s)
CSIT 600
Description
State-of-the-art topics in Information Technology reflecting recent developments in techniques and tools.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify the major issues and technical problems in a specific topic in information technology.
- 2.
  Recognize key technical solutions to the problems in the topic.
- 3.
  Solve specific problems in the topic.
CSIT 6910
Independent Project
[1-3 credit(s)]
Previous Course Code(s)
CSIT 691
Description
An independent project carried out under the supervision of a faculty member. This course may be repeated for credit.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify the state of the art of the topic.
- 2.
  Gain experience in doing research or self-learning.
- 3.
  Solve real-world problems independently.

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