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IDATA 2302 — Algorithms and Data Structures

About This Course

  • Welcome!
  • Course Overview
  • Practicalities
  • Examination
  • Additional Resources

Modules

  • Foundations
    • 1. Computations
    • 2. Computer
    • 3. Correctness
    • 4. Efficiency
    • 5. Algorithm Analysis
    • 6. The Big-O Notation
  • Sequences
    • 1. Abstract Data Types
    • 2. Using Arrays
    • 3. Using Dynamic Arrays
    • 4. Searching
    • 5. Simple Sorting
    • 6. Stacks, Queues, and Deques
  • Recursion
    • 1. Procedure Calls
    • 2. Recursion
    • 3. Linked Lists
    • 4. Quick Sort
    • 5. Merge Sort
    • 6. Sorting Without Comparing
  • Hashing
    • 1. What Is Hashing?
    • 2. Collisions
  • Trees
    • 1. Trees
    • 2. Binary Search Tree
    • 3. Binary Heaps
    • 4. Self-Balancing Trees
    • 5. B-Trees
    • 6. Prefix and Suffix Trees
  • Graphs
    • 1. Graph Theory
      • 1.7.1. Edge List Pattern in Ruby
      • 1.7.2. Adjacency List in JavaScript
      • 1.7.3. Adjacency Matrix in C
    • 2. Reachability & Shortest Paths
    • 3. Minimum Spanning Trees
  • Optimization
    • 1. Exhaustive Search
    • 2. Heuristic Search
    • 3. Dynamic Programming
  • Computational Complexity
    • 1. String Matching
    • 2. Regular Expressions
    • 3. Complexity Classes
  • Beyond RAM
    • 1. Parallel Computing
    • 2. Quantum Computing
    • 3. How to Design Algorithms?

Lab Sessions

  • Setup
  • Foundations
  • Sequences
  • Recursion
  • Trees
  • Graphs
  • Optimization

Recaps

  • Math Recap
  • .rst

Trees

Contents

  • Contents

Version: 0.3.38

Trees#

This module focuses on trees, probably the most ubiquitous structure in Computer Science. We will look at the general concept of tree, and at several data structures around this theme.

Contents#

  • 1. Trees
  • 2. Binary Search Tree
  • 3. Binary Heaps
  • 4. Self-Balancing Trees
  • 5. B-Trees
  • 6. Prefix and Suffix Trees

previous

2. Collisions

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1. Trees

Contents
  • Contents

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