1. Algorithms  Problem Solving and Important problem typesFundamental Data StructuresAsymptotic Notations and Basic Efficiency classesAnalysis of Recursive and NonRecursive AlgorithmsProbabilityRandom Variables and Expectations, Moments and Deviations, distributions, conditional probability, Bayes Theorem Tail Bounds, Chernoff Bound .
2. Problem Solving Techniques Brute force, divide and conquer, decrease and conquer, transform and conquer, dynamic programming, greedy technique.
3. Limitations of Algorithm power P, NP and NP complete problems Back tracking , branch and bound and approximations algorithms probabilistic analysis , Randomized algorithms, Birthday Paradox, Quick sort, bucket sort, minicut, median finding Random graphs, Ramsey number, Hamiltonian cycles.
4. Modern Algorithms Markov chain, stochastic process, page rank Components of evolutionary algorithms, ACO,PCO, TSP problem solving.
5. Algorithms in evolving data streams Sampling, sketching, data stream models, readwrite streams, streamsort, mapreduce Large Graph and Social Networks, Parallel Clustering algorithm for large Data sets with Applications.

1. Introduction
to Algorithms (3rd Ed):Thomas H. Cormen, Charles E.
Leiserson, Ronald L. Rivest and Clifford Stein, MIT
Press (2009)
2. Algorithm Design: Jon Kleinberg and Eva Tardos, AW
(2005)
3. Anany V. Levitin. Introduction to the Design &
Analysis of Algorithms (2nd Ed): A W (2006)
4. Randomized Algoritms: Rajeev Motwani and Prabhakar
Raghavan, Cambridge University Press; Reprint edition
(2010)
5. Data Streams: Algorithms and Applications: S. Muthukrishnan,
Now Publishers (2005)
6. Data Streams: Models and Algorithms: Charu C. Aggarwal,
Springer (2006)
7. Introduction to evolutioary computing: Agoston E.
Eiben, J.E. Smith, Springer (2010)
