Introduction to Algorithms
An algorithm is a well-defined procedure that allows a computer to solve a problem. Another way to describe an algorithm is a sequence of unambiguous instructions. The use of the term 'unambiguous' indicates that there is no room for subjective interpretation. In this module you will get an introduction about the different design strategies in programming.
- 1 Video
- 5 Hours
- 41 Problems
Algorithm Analysis - I
The complexity of the algorithm is analysed with respect to time and space. In this module, you will learn about analysing the complexity of a given problem with respect to time and space.
- 1 Video
- 8 Hours
- 38 Problems
Algorithm Analysis - II
This is the continuation of the previous module. In this module you will see some more examples on analysing the time and space complexity.
- 1 Video
- 8 Hours
- 36 Problems
Searching Algorithms
A search algorithm is any algorithm which solves the search problem, namely, to retrieve information stored within some data structure, or calculated in the search space of a problem domain. In this module, you will learn the working of the searching algorithms like, linear search and binary search. You will also analyse the complexities of the algorithm.
- 1 Video
- 8 Hours
- 38 Problems
Sorting Algorithms
A sorting algorithm is an algorithm that puts elements of a list in a certain order. The most frequently used orders are numerical order and lexicographical order. In this module, you will learn how the given data is sorted using the various sorting algorithms. You will also analyse the complexities of the algorithm.
- 3 Videos
- 5 Hours
- 52 Problems
Design Strategies - Introduction
Following a strategy to solve a problem will make the problem simple as well as reduce the complexity of the solution. In this module, you will learn the introduction to the five different design strategies.
Divide and Conquer Design Strategy
Divide and conquer is an algorithm design paradigm based on multi-branched recursion. A divide and conquer algorithm works by recursively breaking down a data-set into two or more sub-data-sets, until these become simple enough to be solved directly . In this module you will learn how to solve problems using this strategy.
- 1 Video
- 6 Hours
- 78 Problems
Divide and Conquer Design Strategy - II
This module the continuation of the previous module. In this module, you will learn and solve few more example problems about this strategy.
Greedy Strategy - I
A greedy algorithm is an algorithmic paradigm that follows the problem solving heuristic of making the locally optimal choice at each stage with the intent of finding a global optimum. In many problems, a greedy strategy does not usually produce an optimal solution, but nonetheless a greedy heuristic may yield locally optimal solutions that approximate a globally optimal solution in a reasonable amount of time. In this module you will learn how to solve problems using this strategy.
Greedy Strategy - II
This module the continuation of the previous module. In this module, you will learn and solve few more example problems about this strategy.
Dynamic Programming - I
Dynamic programming refers to simplifying a complicated problem by breaking it down into simpler sub-problems in a recursive manner. In this module you will learn how to solve problems using this strategy.
- 1 Video
- 6 Hours
- 71 Problems
Dynamic Programming - II
This module the continuation of the previous module. In this module, you will learn and solve few more example problems about this strategy.
Backtracking - I
Backtracking strategy is defined as a general algorithmic technique that considers searching every possible combination in order to solve an optimization problem. In this module you will learn how to solve problems using this strategy.
Backtracking - II
This module the continuation of the previous module. In this module, you will learn and solve few more example problems about this strategy.
Branch and Bound Strategy
Branch and bound is an algorithm paradigm that basically relies on a combinatorial approach and is hence used for combinatorial optimization problems. For example, the knapsack problem. In this module you will learn how to solve problems using this strategy.
String based problems
In this module, you will learn about solving various string based problems that are used to try out different methods to reduce the complexity with respect to time and space.
