If you are genuinly interested in this topic ignore the lectures, they’re dull, slow, generally uninformative and in Java instead I suggest you buy “Mastering Algorithms in C” http://www.oreilly.com/catalog/masteralgoc/index.html read it and have a play with some of the code. You’ll learn more, I’m sure. I learnt more from a two hour read of small sections than from a whole term of this module.
1. Course Overview – 2 Lectures
An introduction into time/space complexity. Issues of correctness as they relate to the definition of ADTs. The key ideas of abstraction and encapsulation. Notations for describing ADTs. Review of Java support for their implementation: packages, exceptions and interfaces.
2. Introduction to Complexity – 3 Lectures
O() notation, growth rates. Measurement of execution time of some real programs and estimation of their time complexity. Some examples of time/space trade-offs.
3. Classes of Algorithm – 4 Lectures
An overview will be given on the different classes of algorithm; for example, divide and conquer and greedy algorithms. Genetic algorithms will also be discussed. P and NP.
4. Recursion – 2 Lectures
An introduction to recursive thinking. Examples of recursion.
5. Storing and Retrieving Data by Key (1) – 12 Lectures
This problem will be used to motivate the discussion of a wide variety of different implementation techniques. The features of some typical solutions will be related to the dimensions of the problem such as the volume of data to be handled, volatility and the operations required. Internal Storage: linear and binary searching. Linked representations; an introduction to hashing, binary search trees, AVL trees and heaps.
6. Storing and Retrieving Data by Key (2): External storage – 4 Lectures
Performance issues. Hashing and B-tree organisations. The Hashable class in Java.
7. Representing Text – 4 Lectures
String matching algorithms and their performance. Search engines case study.
8. Sorting – 4 Lectures
A comparison of divide and conquer, priority queue and address calculation based sorting algorithms. Performance characteristics of these algorithms will be discussed.
9. Representing Complex Relationships: Graphs – 6 Lectures
Some examples of greedy algorithms. Terminology and implementation considerations. A look at some graph-related problems such as: finding a route (shortest paths); planning a communications network (minimum spanning trees); network routing management (flow graphs); compiling a program or planning a project (topological sorting).
10. Patterns – 3 lectures
An introduction to Object Oriented Design Patterns. The relationship between ADTs and petterns. How patterns may be implemented in Java.