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Introduction to probabilistic methods, information theory and data communication networks.
The first part introduces students to the basic probabilistic methods of Computer Science. These methods belong to the theoretical foundations, or have found applications, in numerous areas of Computer Science, including: computer vision, data and image compression, data communication networks, fault tolerant computing, human-computer interaction, machine learning, neural networks, pattern recognition, etc. The topics discussed include basic concepts of probability theory and its applications in system reliability and network modelling, generation of random numbers by computers, and statistical analysis of experimental data.The second part of the course covers introductory topics of information theory. Using arguments of this theory, we discuss properties and implementations of data encoding schemes, and basic data transformations, such as: data compression, redundant encoding for error protection, and encryption for data security.
(1) COSC121 or COSC123; (2) COSC122; (3) 18 points from Mathematics, Statistics or Engineering Mathematics. MATH101 is not acceptable. MATH115/STAT131/STAT111/STAT112 are strongly recommended.
COSC201
Krzysztof Pawlikowski
Please refer to the department's database for course assessments
Students should check with the department before buying textbooks.
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Domestic fee $384.00
International fee $1,632.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Computer Science and Software Engineering .