Lilly Tech Systems
Trees & Graphs for AI
Master tree and graph data structures through real coding problems with full Python solutions. Every problem connects to AI/ML context — decision trees, computation graphs, DAGs for pipeline orchestration, and knowledge graphs. Build the algorithmic foundation that powers modern AI systems.
Your Learning Path
Follow these lessons in order for a complete understanding of tree and graph algorithms, or jump to any topic that interests you.
1. Trees & Graphs in AI/ML
Why trees and graphs matter for machine learning: decision trees, computation graphs, DAGs for pipeline orchestration, and knowledge graphs. Core terminology and representations.
2. Binary Tree Problems
6 problems with full solutions: inorder, preorder, postorder, and level-order traversals, maximum depth, symmetric tree check, and path sum. ML context for each problem.
3. BST Problems
5 problems: validate BST, kth smallest element, lowest common ancestor, insert and delete operations, and convert sorted array to balanced BST.
4. BFS & DFS
6 problems: number of islands, clone graph, course schedule (cycle detection), word ladder, shortest path in unweighted graph, and connected components.
5. Topological Sort & DAGs
5 problems: course schedule order, task scheduling with dependencies, build order, alien dictionary, and DAG shortest path. Critical for ML pipeline design.
6. Dijkstra & Advanced
5 problems: shortest path with weights, network delay time, cheapest flights within k stops, minimum spanning tree, and union-find for connected components.
7. Tree Construction & Serialization
5 problems: build tree from inorder+preorder, serialize and deserialize binary tree, trie implementation, suffix tree basics, and expression tree evaluation.
8. Patterns & Tips
Tree/graph pattern cheat sheet, visualization techniques for debugging, complexity analysis guide, and FAQ accordion with real interview tips.
What You Will Learn
By the end of this course, you will be able to:
Solve Tree & Graph Problems
Confidently tackle 32+ coding problems covering traversals, BSTs, BFS/DFS, topological sort, shortest paths, and tree construction with optimal Python solutions.
Connect Algorithms to AI/ML
Understand how decision trees split data, how DAGs orchestrate ML pipelines, how computation graphs enable backpropagation, and how knowledge graphs power reasoning.
Recognize Patterns
Identify which tree/graph pattern applies to a new problem: recursive vs iterative, BFS vs DFS, topological order vs shortest path, union-find vs adjacency list.
Ace Coding Interviews
Apply systematic approaches to tree and graph questions in technical interviews with clean, well-commented Python code and optimal time/space complexity.