The curriculum covers topics in algorithms and data structures. Among data structures, it covers advanced topics on trees, heaps, graphs, et cetera. It provides details of computational complexity notations like O(). It covers the correctness and runtime analysis of recursive algorithms using recurrences. These algorithms range from mathematical computations to sorting algorithms. These algorithms are put in the context of appropriate algorithmic paradigms like divide-and-conquer and dynamic programming. Finally, computational complexity classes and problem reductions are introduced along with the proof techniques for NP-hardness and NP-completeness.
Total contact hours: 32
Private study hours: 118
Total study hours: 150
Main assessment methods
2 programming assessments (15 hours each) (25% each)
2 hour unseen written examination (50%)
Reassessment methods
Like for like.
Algorithms. Sedgewick and Wayne
Algorithms. Jeff Erickson
Introduction to Algorithms. Cormen, Leiserson, Rivest, and Stein
The Art of Computer Programming. Donald E. Knuth
The Algorithm Design Manual. Steven S. Skiena
Data Structures and Algorithms in Java 2nd Edition. M.T. Goodrich and R. Tamassia
Algorithms and Data Structures 2nd Edition. Jeffrey H. Kingston x
Cracking the Coding Interview. Gayle Laakmann McDowell
See the library reading list for this module (Canterbury)
On successfully completing the module students will be able to:
1. specify, test, and verify implementations of algorithms;
2. analyse the time and space behaviour of algorithms;
3. analyse and compare general algorithmic paradigms;
4. make informed decisions while choosing data structures and algorithms for practical use;
5. demonstrate an understanding of algorithmic reduction, complexity classes and hardness.
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