About This Course

Recursive algorithms solve problems by repeatedly breaking them into smaller, similar subproblems and solving these until a base case is reached. This technique is common in factorial computation, tree traversal, and divide-and-conquer algorithms. Deterministic algorithms, on the other hand, follow a fixed sequence of steps, ensuring the same output for a given input and are integral to tasks like sorting and searching. Approximation algorithms provide near-optimal solutions for complex problems where exact solutions are computationally expensive, making them vital in optimization scenarios like the traveling salesman problem.

Optimization problems are solved using linear programming, gradient-based methods, or metaheuristics. Genetic algorithms are a metaheuristic inspired by natural evolution, employing selection, crossover, and mutation to evolve optimal solutions. Evolutionary programming focuses on strategy optimization, particularly in non-linear and dynamic environments. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are nature-inspired algorithms that simulate social behaviors to optimize solutions. PSO mimics birds' flocking, and ACO emulates ants' foraging.

Evolutionary programming finds applications in control systems, optimization tasks, and adaptive learning. AI algorithms like neural networks and decision trees are pivotal in data science for predictive analytics and anomaly detection. Modern data science increasingly relies on deep learning, ensemble methods, and distributed algorithms to efficiently handle vast and complex datasets.