1. Algorithms - Problem Solving and Important problem types-Fundamental Data Structures-Asymptotic Notations and Basic Efficiency classes-Analysis of Recursive and Non-Recursive Algorithms-Probability-Random 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, mini-cut, 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, read-write streams, stream-sort, map-reduce -Large Graph and Social Networks, Parallel Clustering algorithm for large Data sets with Applications.
to Algorithms (3rd Ed):Thomas H. Cormen, Charles E.
Leiserson, Ronald L. Rivest and Clifford Stein, MIT
2. Algorithm Design: Jon Kleinberg and Eva Tardos, AW
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
5. Data Streams: Algorithms and Applications: S. Muthukrishnan,
Now Publishers (2005)
6. Data Streams: Models and Algorithms: Charu C. Aggarwal,
7. Introduction to evolutioary computing: Agoston E.
Eiben, J.E. Smith, Springer (2010)