Course Objectives

1. To develop an understanding of the concepts and complexities of algorithms, so the
   students can appreciate the requirement of fast and efficient algorithms. Cover different
   sorting and graph algorithms, along with the concept of theory of NP completeness, so
   that students can identify computationally intractable problems.

2. The main focus will be looking at the algorithms from an applied perspective, this
   includes coding algorithms using efficient data structures, running simulations and
   comparing results.

3. Tasks/capabilities the students should be able to undertake upon completion of
   this course are:

   1. prepare the students for the market as better and smart programmers who are able to differentiate between the tasks that can be solved or not

   2. enable the students to comprehend problems where ever the quality of a solution has to be judged

   3. prepare the students to undertake graduate level course work where they
      may have to take another advance course in algorithms

Course Outline

1. Overview
   1. Introduction to Algorithms
   2. Complexity Analysis
   3. Program Efficiency and Complexity
   4. Theory and Analysis of Recursive Algorithms
   5. Graph Theory
2. Sorting Algorithms
   1. Quick Sort
   2. Heap Sort
   3. External Merge Sort
   4. Linear Time Sorting
3. Data Structures
   1. Elementary Data Structures
   2. Hash Tables
   3. Binary Search Tree
   4. B-Trees
4. Advanced Design and Analysis Techniques
   1. Data Mining
   2. Divide and Conquer Algorithms
   3. Greedy Algorithms
   4. Dynamic Programming
5. Graph Algorithms
   1. Elementary Graph Algorithms
   2. Graph Drawing
   3. Minimum Spanning Trees
   4. Single Source and all pair Shortest Paths
6. NP Completeness
   1. Efficient Reductions
   2. Theory of NP Completeness

Text Books

1. Cormen, Leiserson and Rivest, "Introduction to Algorithms", MIT Press, 1990

2. Aho, Hopcroft and Ullman, "Data Structures and Algorithms", Addison-Wesley, 1983