Computer Science Syllabus - Mathematical Methods for Computer Science
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Lecturer: Dr R.J. Gibbens
No. of lectures: 12
Prerequisite course: Probability
This course is a prerequisite for Computer Graphics and Image Processing (Part IB) and the following Part II courses: Artificial Intelligence II, Bioinformatics, Computer Systems Modelling, Computer Vision, Digital Signal Processing, Information Theory and Coding, Quantum Computing.
The aim of this course is to introduce and develop mathematical methods that are key to many modern applications in Computer Science. The course proceeds on two fronts: (i) probability modelling techniques that allow stochastic systems and algorithms to be described and better understood and (ii) Fourier methods and their generalizations which lie at the heart of modern digital signal processing, coding and information theory. The style of the course is necessarily concise but will attempt to blend a mix of theory with examples and glimpse ahead at applications taken up in Part II courses.
At the end of the course students should
Oppenheim, A.V. & Willsky, A.S. (1997). Signals and systems. Prentice-Hall.
Next: Numerical Analysis I Up: Michaelmas Term 2005: Part Previous: Logic and Proof   Contents Christine Northeast
Sun Sep 11 15:46:50 BST 2005