Course

CS-250: Algorithms I

Lectures in this course (101)

Covers Minimum Spanning Trees, Kruskal's Algorithm, and Shortest Paths in directed graphs.

Explains the Bellman-Ford algorithm for finding the shortest path in a directed graph with edge weights.

Covers the Bellman-Ford and Dijkstra algorithms for finding shortest paths in graphs with different edge weights.

Explores the probabilistic analysis of the hiring problem, focusing on strategies, expected hires, and randomized algorithms.

Explores probabilistic analysis in algorithms, covering the hiring problem and the birthday paradox, along with hash tables and functions.

Covers the analysis of hash tables, including search operations and expected collisions.

Covers the efficient implementation of hash tables and the Quick Sort algorithm.

Covers the analysis of quick sort, including time complexity and randomization for finding the k-th smallest number.

Explores probabilistic analysis of the hiring problem using Indicator Random Variables and clever methods to calculate expected outcomes.

Discusses the expected running time of randomized quicksort and decision trees as an abstraction of comparison sorts.

Explores hash tables through the Birthday Paradox, collisions, and implementation methods.

Explores decision tree analysis for sorting algorithms and introduces the Counting Sort efficiency based on input structure.

Introduces hashing tables, including the Birthday Lemma, Direct-Address Tables, collisions, and Chained-Hash-Search.

Explores the analysis of running time in hash tables, covering successful searches, key distribution, and table size impact.

Explores the Quick Sort algorithm, focusing on its divide-and-conquer approach and time complexity analysis.

Analyzes the running time and comparisons in randomized quick sort, proving its efficiency and optimality in comparison sorting.

Provides a detailed review of the 2016 final exam, covering various algorithmic problems.

Explores maximizing candies in a city, dynamic programming, minimum spanning trees, and stock price prediction strategies.

Offers a recap before the Algorithms exam, covering problem-solving strategies and algorithm implementation with sample problems.

Covers the basics of algorithms, the importance of studying them, data structures, and the impact of algorithms on various fields.