CS-250: Algorithms I

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Lectures in this course (101)

Analysis of Algorithms

Covers the analysis of algorithms, focusing on insertion sort and computational models.

Merge Sort: Divide-and-Conquer Approach

Introduces the merge sort algorithm through the divide-and-conquer approach, emphasizing correctness and time analysis.

Merge Sort: Divide, Conquer, Combine

Explores Merge Sort, a sorting algorithm that divides, conquers, and combines arrays efficiently to achieve O(nlog n) time complexity.

Merge Sort: Divide-and-Conquer

Introduces Merge Sort, a divide-and-conquer algorithm for efficient array sorting, discussing correctness, runtime analysis, linear-time merging, and recurrence solving techniques.

Solving Recurrences and Recursion Trees

Covers techniques for solving recurrences and introduces the Master Theorem.

Solving Recurrences

Covers sorting algorithms, divide-and-conquer analysis, solving recurrences through induction, complexities of floors and ceilings, and the Master method.

Maximum Subarray Problem

Covers the Master method, maximum-subarray problem, and divide-and-conquer algorithmic paradigm.

Matrix Multiplication: Divide-and-Conquer

Covers divide-and-conquer in matrix multiplication, recursion trees, the master method, maximum-subarray problem, and smart algorithm.

Matrix Multiplication and Heap Data Structure

Covers the divide-and-conquer algorithm for matrix multiplication and introduces the (binary) heap data structure.

Matrix Multiplication: Divide-and-Conquer

Explores the Divide-and-Conquer algorithm for matrix multiplication, including Strassen's Method and its significance in optimizing time complexity.

Heapsort and Priority Queues

Covers the Heapsort algorithm, which sorts arrays efficiently using max-heaps and introduces priority queues.

Heaps: Data Structure and Heapsort

Explores heaps, heapsort, and their efficiency in implementing priority queues.

Elementary Data Structures: Stacks, Queues, Linked Lists

Covers stacks, queues, and linked lists, emphasizing efficiency and dynamic structures.

Binary Search Trees: Implementation and Operations

Covers the implementation and operations of basic data structures like stacks, queues, and linked lists, and introduces binary search trees.

Binary Search Trees: Operations and Procedures

Covers operations and procedures related to binary search trees, including finding successors and predecessors.

Binary Search Trees: Operations and Implementations

Explores Binary Search Trees, covering operations like searching, finding min/max, successors, and printing.

Dynamic Programming: Fibonacci Numbers

Covers dynamic programming with a focus on Fibonacci numbers and efficient calculation algorithms.

Dynamic Programming: Fibonacci Numbers

Covers dynamic programming with a focus on Fibonacci numbers and the rod cutting problem.

Dynamic Programming: Memoization and Bottom-Up Approach

Explores dynamic programming through memoization and a bottom-up approach to optimize recursive algorithms.

Dynamic Programming: Rod Cutting and Matrix Chain Multiplication

Introduces dynamic programming with a focus on rod cutting and matrix chain multiplication.

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