2412 - Study guides, Class notes & Summaries

This document contains detailed notes from Lecture 11 of the CO2412 course on Computational Thinking. The lecture focuses on graph traversal, a fundamental concept in computer science that involves visiting and updating each vertex in a graph. The notes cover the two primary methods of graph traversal: Breadth-First Search (BFS) and Depth-First Search (DFS).

This document contains detailed notes from Lecture 5 of the CO2412 course on Computational Thinking. The lecture provides a comprehensive review of Big O notation, a crucial concept for estimating the time and space efficiency of algorithms, particularly in worst-case scenarios. The notes focus on simplifying complexity estimations by highlighting the fastest-growing terms and discarding less significant details.

This document contains detailed notes from Lecture 9 of the CO2412 course on Computational Thinking. The lecture provides an in-depth exploration of Binary Search Trees (BSTs) and Heaps, two fundamental data structures in computer science.

This document contains detailed notes from Lecture 17 of the CO2412 course on Computational Thinking, focusing on the fundamentals of propositional logic. The lecture provides a thorough introduction to propositional logic, which is used to assess the validity of arguments by applying logical rules to determine whether conclusions drawn from premises are sound.

This document contains comprehensive notes from Lecture 14 of the CO2412 course on Computational Thinking. The lecture introduces the fundamental concepts of probability, a crucial area in mathematics and statistics that measures the likelihood of events occurring. These notes cover the different types of probability, including classical, frequentist, and subjective probability, providing clear definitions and examples for each.

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This document contains detailed notes from Lecture 12 of the CO2412 course on Computational Thinking. The lecture focuses on pathfinding algorithms, essential tools in computer science for determining the shortest or most efficient path between two points within a graph structure. These algorithms have broad applications, including robotics, logistics, game development, and network routing.

This document contains comprehensive notes from Lecture 8 of the CO2412 course on Computational Thinking. The lecture focuses on the concept of backtracking, a powerful problem-solving technique that incrementally builds solutions and backtracks when constraints are violated.

This document contains comprehensive notes from Lecture 4 of the CO2412 course on Computational Thinking. The lecture focuses on the crucial topic of performance evaluation, highlighting the importance of understanding algorithm efficiency to make informed decisions during algorithm design and optimization. The notes outline the steps involved in creating efficient programs, from selecting the best algorithm and data structure to identifying bottlenecks and optimizing relevant parts of the code.