Lilly Tech Systems
Competitive Programming for AI
HackerRank and Codeforces style problems with AI/ML twists. This course covers mathematical problems, string algorithms, advanced graph algorithms, advanced data structures, and full contest-style problems set in machine learning contexts. Every problem includes optimal solutions with complete Python code, complexity analysis, and step-by-step explanations.
Your Learning Path
Follow these lessons in order for a complete competitive programming bootcamp, or jump to any topic you need to sharpen.
1. CP for ML Engineers
Why competitive programming matters for AI/ML roles, top platforms, contest formats, rating systems, and a strategic approach to contest preparation.
2. Mathematical Problems
Five problems covering modular arithmetic, matrix exponentiation, number theory, combinatorics, and probability — the math foundations of competitive programming.
3. String Algorithms
Five problems covering KMP pattern matching, Rabin-Karp hashing, trie operations, suffix arrays, and string hashing for NLP and text processing contexts.
4. Advanced Graph Algorithms
Five problems covering Bellman-Ford, Floyd-Warshall, max flow, bipartite matching, and strongly connected components in AI pipeline and network contexts.
5. Advanced Data Structures
Five problems covering segment trees, BIT/Fenwick trees, disjoint set union, sparse tables, and balanced BSTs for efficient query processing in ML systems.
6. Contest-Style Problems
Five full contest problems with AI/ML context: data pipeline optimization, model selection, hyperparameter grid search, feature selection, and resource scheduling.
7. Contest Strategy & Tips
Time management, debugging techniques, template code for fast submissions, and a comprehensive FAQ accordion for competitive programming success.
What You Will Learn
By the end of this course, you will be able to:
Solve Contest Problems Fast
Apply optimal algorithms and data structures to solve HackerRank and Codeforces style problems under time pressure, with clean and efficient code.
Master Core Algorithms
Implement KMP, Bellman-Ford, max flow, segment trees, and other advanced algorithms from scratch with full understanding of their internals.
Apply AI/ML Twists
Solve problems framed in machine learning contexts: data pipelines, model selection, feature engineering, hyperparameter optimization, and GPU scheduling.
Win Contests Confidently
Use proven contest strategies for time management, debugging, and template code to maximize your score in competitive programming competitions.