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Informatics II - Algorithms & Data Structures

A comprehensive course covering search and sorting algorithms, recursion, and essential data structures through hands-on implementations in C.

Course Overview

This repository contains laboratory assignments and implementations from the Informatics II course at the University of Zurich (Spring Semester 2024). The course provides in-depth coverage of algorithmic thinking and data structure design through practical C implementations, building a strong foundation in computer science fundamentals.

Grade: 5.25/6.0 | ECTS: 6

Topics Covered

• Sorting Algorithms: Bubble Sort, Insertion Sort, Quick Sort, Heap Sort
• Search Algorithms: Linear and binary search implementations
• Algorithmic Analysis: Complexity analysis, Big-O notation, correctness proofs, and recurrence relations
• Recursion: Problem decomposition and recursive algorithm design
• Divide and Conquer: Algorithm paradigm with recurrence relation solving
• Linear Structures: Arrays, Linked Lists, Stacks, Queues, Abstract Data Types (ADTs)
• Trees: Binary Trees, Binary Search Trees (BST), AVL Trees, Red-Black Trees
• Hash Tables: Hash functions and collision resolution
• Memory Management: Pointers, manual memory allocation, and low-level C programming

Repository Structure

├── Lab 0 - C-Environment/
├── Lab 1 - Introduction to C, Basic Sorting/
├── Lab 2 - Recursion/
├── Lab 3 - Complexity, Correctness/
├── Lab 4 - Divide and Conquer, Recurrence/
├── Lab 5 - Heap Sort, Quick Sort/
├── Lab 6 - Pointers, Linked Lists/
├── Lab 7 - ADTs, Stacks, Queues/
├── Lab 8 - Binary Trees, BST/
├── Lab 9 - Red-Black Trees/
└── Lab 10 - Hash Tables/

Format

Each laboratory directory contains:

• C source files implementing the covered algorithms and data structures
• Executable binaries and debug symbols (dSYM for macOS)
• Hands-on implementations demonstrating theoretical concepts
• Progressive difficulty building from basic sorting to advanced tree structures

Key Learning Outcomes

• Understand and apply time and space complexity analysis
• Implement fundamental algorithms and compare their performance
• Design and implement custom data structures from scratch
• Apply the Divide and Conquer paradigm and solve recurrence relations
• Work effectively with pointers and dynamic memory management
• Analyze algorithmic correctness and prove algorithm properties
• Build complex data structures: Trees, Hash Tables, and ADTs

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Course instructors: Michael Hanspeter Böhlen and team