Selected Topics in Algorithms | Advanced Algorithm Course | IIT Kharagpur
Course Details
| Exam Registration | 84 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Computer Science and Engineering |
| Credit Points | 3 |
| Level | Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 25 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Master Advanced Algorithm Design with IIT Kharagpur's Premier Course
In the realm of computer science, the efficiency of an algorithm can mean the difference between a solution that takes seconds and one that takes centuries. While foundational courses cover essential techniques like greedy algorithms and dynamic programming, tackling modern computational challenges requires a deeper, more sophisticated toolkit. This is where the Selected Topics in Algorithms course, offered by the prestigious Indian Institute of Technology, Kharagpur, comes into play.
Designed for postgraduate students and advanced undergraduates, this intensive 12-week program delves into the advanced paradigms that power today's most complex software systems and research. Taught by Prof. Palash Dey, an expert in algorithmic game theory, this course bridges the gap between basic algorithmic knowledge and cutting-edge research-level techniques.
About the Instructor: Prof. Palash Dey
Prof. Palash Dey brings a wealth of academic excellence and research expertise to this course. As an Assistant Professor in the Department of Computer Science and Engineering at IIT Kharagpur since 2018, his primary research focus is algorithmic game theory. His academic journey includes:
- Ph.D. & M.E. from the Department of Computer Science and Automation, Indian Institute of Science (IISc), Bangalore.
- B.E. in Computer Science and Engineering from Jadavpur University.
- Post-doctoral experience as an INSPIRE faculty at the Tata Institute of Fundamental Research (TIFR), Mumbai.
His deep theoretical grounding ensures that students learn not just how algorithms work, but the profound mathematical principles that make them efficient.
Who Should Take This Course?
This course is meticulously designed for:
- Postgraduate students in Computer Science and related fields.
- Advanced undergraduate students seeking to challenge themselves beyond the standard curriculum.
- Professionals and researchers aiming to solidify their understanding of advanced algorithmic concepts.
Prerequisite: A solid understanding of basic algorithms and data structures is required to fully benefit from this advanced material.
Industry Relevance
The techniques covered in this course form the backbone of problem-solving at top technology firms. Companies like Google, Microsoft, Amazon, and other leading software giants consistently deal with problems related to network optimization, large-scale data processing, and finding efficient approximate solutions to NP-hard problems. Mastery of these topics is invaluable for anyone targeting a career in cutting-edge software development, research, or data science.
Detailed 12-Week Course Layout
The course is structured to build your knowledge systematically, from fundamental advanced concepts to specialized algorithmic paradigms.
Weeks 1-2: Network Flows
Dive into one of the most powerful algorithmic paradigms for modeling and solving optimization problems.
- Ford-Fulkerson and Edmonds-Karp Algorithms
- Max-Flow Min-Cut Theorem
- Applications in matching (Edmonds' Matching Algorithm) and other domains.
Weeks 3-6: Randomization in Algorithms
Discover how introducing randomness can lead to astonishingly simple, elegant, and efficient algorithms.
- Karger's Min-Cut Algorithm
- Randomized Algorithm for 2-SAT
- Polynomial Identity Testing and the Schwartz-Zippel Lemma
- Concentration Inequalities (Markov, Chebyshev, Chernoff)
- Markov Chains, Random Walks, and the Monte Carlo Method
Week 7: NP-Completeness
Formally understand the class of problems for which no efficient exact solution is known, setting the stage for approximation techniques.
Weeks 8-10: Approximation Algorithms
Learn how to design efficient algorithms that provide provably near-optimal solutions for NP-hard problems.
- Classical problems: Vertex Cover, Set Cover, Travelling Salesman Problem (TSP)
- APTAS for Bin Packing, FPTAS for Knapsack
- Using Linear Programming as a tool: Rounding techniques and the Primal-Dual Schema
Weeks 11-12: Parameterized Algorithms
Explore a modern framework for tackling NP-hard problems by confining the exponential complexity to a specific parameter of the problem.
- Fixed Parameter Tractable (FPT) Algorithms
- Kernelization
- Bounded Search Tree, Iterative Compression, and Color Coding techniques.
Essential Reference Books
The course draws from some of the most authoritative texts in the field:
| Book Title | Authors | Primary Focus |
|---|---|---|
| Approximation Algorithms | Vijay V. Vazirani | The definitive guide to approximation algorithm design and analysis. |
| Introduction to Algorithms | Cormen, Leiserson, Rivest, Stein | Comprehensive reference for fundamental and advanced algorithms (CLRS). |
| Parameterized Algorithms | Cygan, Fomin, et al. | Modern and extensive treatment of parameterized complexity and algorithms. |
| Probability and Computing | Mitzenmacher and Upfal | Essential for understanding randomized algorithms and probabilistic analysis. |
Why Enroll in This Course?
This course is more than a series of lectures; it's an intellectual journey into the heart of computational problem-solving. You will transition from understanding algorithms to designing them. You'll learn not only to implement solutions but to analyze their limits and prove their correctness and efficiency. For aspiring researchers, software engineers at top tech firms, or anyone fascinated by the power of efficient computation, this course offers an unparalleled opportunity to learn from one of India's premier institutions and an expert in the field.
Embrace the challenge and deepen your mastery of the algorithms that shape our digital world.
Enroll Now →