Design and Analysis of Algorithms Course | NPTEL | Prof. Madhavan Mukund
Course Details
| Exam Registration | 5237 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 8 weeks |
| Categories | Computer Science and Engineering, Foundations of Computing |
| Credit Points | 2 |
| Level | Undergraduate |
| Start Date | 19 Jan 2026 |
| End Date | 13 Mar 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 16 Feb 2026 |
| Exam Date | 28 Mar 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Unlock the Power of Efficient Problem-Solving: A Deep Dive into Algorithm Design
In the world of computer science, the ability to design and analyze efficient algorithms is the cornerstone of creating powerful, scalable software. Whether you're building the next-generation search engine, optimizing logistics for a global company, or developing cutting-edge AI, a strong foundation in algorithms is non-negotiable. For students and professionals looking to master this critical skill, the Design and Analysis of Algorithms course, taught by the eminent Prof. Madhavan Mukund of the Chennai Mathematical Institute, stands as a premier educational opportunity.
Learn from an Academic Leader: Prof. Madhavan Mukund
This course is guided by an instructor of exceptional caliber. Prof. Madhavan Mukund brings decades of research and teaching excellence to the table. An alumnus of IIT Bombay (BTech) and Aarhus University (PhD), he has been a pillar of the Chennai Mathematical Institute since 1992, where he currently serves as Professor and Director. His research in formal verification is internationally recognized. Beyond academia, Prof. Mukund has held prestigious leadership roles, including President of the ACM India Council and the Indian Association for Research in Computing Science (IARCS). His deep commitment to computing education is evident through his long-standing role as the National Coordinator of the Indian Computing Olympiad. Learning algorithms from such a distinguished expert ensures you're gaining insights from the very forefront of the field.
Course Overview: What You Will Learn
This meticulously structured 8-week undergraduate-level course is designed to take you from fundamental concepts to advanced topics in algorithm design. It is perfectly suited for BE/BTech Computer Science students in their 2nd or 3rd year, or for any developer looking to solidify their theoretical foundations.
Prerequisites: A basic understanding of programming and data structures is recommended to get the most out of this course.
Industry Relevance: The skills taught are universally valued across the software industry, making this course highly beneficial for anyone targeting a career in software services, product development, or tech research.
Weekly Curriculum Breakdown
The course is divided into a logical progression of modules, each building upon the last. Here’s a detailed look at the 8-week journey:
| Week | Core Topics Covered | Assignment Focus |
|---|---|---|
| Week 1 | Introduction, Motivation, Asymptotic Complexity (Big O notation) | MCQ/Fill in blanks |
| Week 2 | Searching & Sorting: Binary Search, Insertion, Selection, Merge, and Quicksort | MCQ and Programming Assignment |
| Week 3 | Graph Algorithms: BFS, DFS, DFS applications, Directed Acyclic Graphs | MCQ and Programming Assignment |
| Week 4 | Shortest Paths (Dijkstra), Minimum Spanning Trees (Prim, Kruskal), Union-Find | MCQ and Programming Assignment |
| Week 5 | Divide and Conquer, Priority Queues & Heaps, Introduction to Search Trees | MCQ and Programming Assignment |
| Week 6 | Search Tree Operations & Balancing, Greedy Algorithms, Dynamic Programming Intro | MCQ and Programming Assignment |
| Week 7 | Dynamic Programming: Edit Distance, LIS, Matrix Multiplication, Bellman-Ford, Floyd-Warshall | MCQ and Programming Assignment |
| Week 8 | Intractability and NP-Completeness: Reductions and Examples | MCQ/Fill in blanks |
Key Learning Outcomes and Design Techniques
By the end of this course, you will have a firm grasp of:
- Asymptotic Analysis: The language of algorithm efficiency using Big O, Omega, and Theta notations.
- Fundamental Algorithms: Mastery of essential sorting and searching techniques.
- Graph Theory Essentials: Ability to navigate, search, and find optimal paths and trees within graph structures.
- Algorithmic Design Paradigms: Hands-on experience with the three major design techniques:
- Divide and Conquer (e.g., Merge Sort, Closest Pair)
- Greedy Algorithms (e.g., Interval Scheduling, Huffman Coding)
- Dynamic Programming (e.g., Weighted Interval Scheduling, Edit Distance)
- Essential Data Structures: Deep understanding of heaps, union-find, and balanced search trees and their role in algorithm optimization.
- Understanding Intractability: Introduction to the theory of NP-completeness, teaching you to identify problems that are inherently difficult to solve efficiently.
Why This Course is a Must for Aspiring Programmers
This course moves beyond simple coding to teach you how to think about computational problems. You won't just learn how to implement Dijkstra's algorithm; you'll understand why it works, how to analyze its efficiency, and in which scenarios it applies. This theoretical foundation is what separates competent coders from exceptional software engineers and architects. The blend of theoretical modules with practical programming assignments ensures you can both reason about and implement sophisticated algorithms.
If you are ready to build a rigorous understanding of how to solve complex problems efficiently, the Design and Analysis of Algorithms course by Prof. Madhavan Mukund is your definitive starting point. It equips you with the intellectual tools needed to excel in academic pursuits, competitive programming, and a successful career in technology.
Enroll Now →