Merge sort

Worst-Case Analysis

Solving Recurrences

Median and Selection

Quick Sort

Lower Bounds for Sorting

Red-Black Trees

Graphs

Depth-first search (DFS)

Dijkstra's algorithm

Dynamic programming

Longest Common Subsequence (LCS)

Independent Set

Ford–Fulkerson algorithm

Gale–Shapley algorithm

Minimum Spanning Tree (MST)

Asymptotics

Randomized Algorithms

Bucket Sort

Hashing

Breadth-first search (BFS)

Strongly connected components

Bellman–Ford algorithm

Floyd–Warshall algorithm

Knapsack problem

Greedy Algorithm

Max-Flow

Stable Matching

Binary Search Tree (BST)

Master theorem

CS 161 Design and Analysis of Algorithms Stanford University Winter 2023 This course provides an in-depth exploration of algorithm analysis and design. It covers various sorting, searching, and selection algorithms, data structures, and fundamental graph algorithms. It emphasizes the understanding of worst and average case analysis, recurrences, and asymptotics.  This course will cover the basic approaches and mindsets for analyzing and designing algorithms and data structures. Topics include the following: Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, and randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow and string searching. Additional Calculus for Engineers (ACE) is one of the School of Engineering’s Equity and Inclusion Initiatives, designed to provide the skills and solid foundation in mathematics, computational math in engineering, and computer science to undergraduate students interested in pursuing an engineering degree. The goal of ACE is to increase confidence and content knowledge through small group interactive sessions and additional academic resources provided to students enrolled in the program. We especially want to provide an opportunity for students who come from educationally disadvantaged backgrounds or for anyone who feels they might need additional support in order to succeed. We limit enrollment to enable small classes that allow students to have one-on-one interactions with the CA. CS 106B or CS 106X; CS 103 or CS 103B; CS 109 or STATS 116. ### Textbook Information

All the following textbooks are optional. They are all excellent for supplementing lectures with details.

- *Introduction to Algorithms (3rd Ed)*, Cormen, Leiserson, Rivest, &amp; Stein ([available online!](https://searchworks.stanford.edu/view/12846639))
- *Algorithms*, Dasgupta, Papadimitriou, &amp; Vazirani
- *Algorithm Design*, Kleinberg &amp; Tardos
- *Algorithms Illuminated*, Roughgarden ([with great videos!](https://www.youtube.com/channel/UCcH4Ga14Y4ELFKrEYM1vXCg/playlists))

All students should retain receipts for books and other course-related expenses, as these may be qualified educational expenses for tax purposes. If you are an undergraduate receiving financial aid, you may be eligible for additional financial aid for required books and course materials if these expenses exceed the aid amount in your award letter. For more information, review your award letter or visit the [Student Budget website](https://financialaid.stanford.edu/undergrad/budget/index.html).

CS 161 Design and Analysis of Algorithms

Data Structures and Algorithms

File I/O

Data structure

Software testing

Computer memory

Set (abstract data type)

List (abstract data type)

Machine learning

Hash table

Tree (data structure)

Object (computer science)

Polymorphism (computer science)

Exception handling

Debugger

Class (computer programming)

Inheritance (object-oriented programming)

Object-Oriented Programming (OOP)

Linked list

Web scraping

Binary tree

Web API

Sorting algorithm

Random testing

Binary expression tree

Recursion (computer science)

CSCI 0112 - Computing Foundations: Program Organization Brown University Fall 2022 CSCI 0112 progresses from CSCI 0111, focusing on structuring programs for solving isolated subproblems. It delves into various algorithms, implementations from abstract descriptions, data organization methods, and program efficiency. Ethical considerations in software development are underscored. Topics include data structures, OOP, web APIs, machine learning, and more. CSCI 0112 builds on the data-focused programming introduced in CSCI 0111. Students will learn how to structure their programs so that subproblems can be solved in isolation. We will discuss several algorithms–abstract descriptions of computations–and students will get practice implementing working programs from these abstract descriptions. We will continue our study of data organization, covering several new ways of storing and accessing information. We will also continue to discuss program efficiency; students will learn how to reason about the performance of their programs. Finally, like CSCI 0111, CSCI 0112 will foreground the ethical considerations programmers must think about when developing software.   

CSCI 0112 - Computing Foundations: Program Organization

Computer Programming

Union-Find

Stack (abstract data type)

Java

Priority Queues

Geometric Applications of BSTs

Mincuts

Trie

Algorithm Design

Analysis of Algorithms

Queue (abstract data type)

Elementary Sorts

Elementary Symbol Tables

Balanced Search Trees

Digraphs

Maxflows

String Sorts

Data Compression

Shortest paths

COS 226 Algorithms and Data Structures Princeton University Spring 2023 This course surveys crucial algorithms and data structures used in modern computing, with emphasis on sorting, searching, graphs, and strings. It aims to develop implementations, understand their performance, and evaluate their effectiveness. This course surveys the most important algorithms and data structures in use on computers today. Particular emphasis is given to algorithms for sorting, searching, graphs, and strings. The course concentrates on developing implementations, understanding their performance characteristics, and estimating their potential effectiveness in applications.  COS 126 or approval by the COS placement officer. **Textbook**. *Algorithms, 4th edition* by Robert Sedgewick and Kevin Wayne. Addison-Wesley Professional, 2011, ISBN 0-321-57351-X. The assigned readings are required.

COS 226 Algorithms and Data Structures

Merge sort

3 courses cover this concept

CS 161 Design and Analysis of Algorithms

CSCI 0112 - Computing Foundations: Program Organization

COS 226 Algorithms and Data Structures