Advanced DSA | Algorithms for AI

📄️ Backtracking

Systematic depth-first exploration of a decision tree that prunes dead branches early and undoes each choice.

From first principles to production DP — memoization, tabulation, and the classic optimization families.

Graph representation, traversal, and the core algorithms that everything else in graphs builds on.

Dijkstra's algorithm and A* — from theory to practical pathfinding across DS and AI systems.

Zero-to-expert on DSU: path compression, union by rank, complexity proofs, and classic problems.

A practitioner-grade reference on greedy correctness proofs and interval scheduling patterns.

Prefix-tree structures for string retrieval, autocomplete, and the bridge to tokenization.

Range query/update structures — intuition for beginners, rigor and constants for experts.

Interactive practice for Backtracking — animated walkthrough and a live Python exercise.

Interactive practice for Dynamic Programming — animated walkthrough and a live Python exercise.

Interactive practice for Graph Theory — a live in-browser Python exercise.

Interactive practice for Greedy Algorithms — a live in-browser Python exercise.

Interactive practice for Segment Tree & Fenwick Tree — a live in-browser Python exercise.

Interactive practice for Shortest Path Algorithms — a live in-browser Python exercise.

Interactive practice for Tries (Prefix Trees) — a live in-browser Python exercise.

Interactive practice for Union-Find — animated walkthrough and a live Python exercise.