Graph Algorithms Course Description

Course Title: Learning Graph Algorithms with AI

Course Description

This 10-week course is designed for college students eager to learn how graph algorithms can be applied to solve real-world business problems. A distinctive feature of this course is its integration of generative AI tools, enabling students to generate code, create visualizations, and experiment with algorithms in real time.

Prerequisites

Students should have a foundational understanding of data structures and databases. Access to a web browser and a software development environment capable of running Python code is required. While familiarity with Python and JavaScript is beneficial, it is not mandatory.

Course Objectives

By the end of this course, students will be able to:

1. Remember: Define key graph components (nodes, edges, properties) and basic graph types (e.g., directed, undirected, acyclic, concept graphs).
2. Understand: Explain and compare the RDF and LPG data models, discussing their relative strengths and weaknesses.
3. Apply: Use generative AI tools to generate graph data and algorithms for real-world business scenarios.
4. Analyze: Distinguish between various types of graph algorithms (e.g., depth-first search, clustering, page rank, similarity, centrality), and identify their use cases.
5. Evaluate: Assess the suitability of different graph algorithms for specific business problems and justify algorithm choices.
6. Create: Develop and refine custom graph algorithms using browser-based libraries like D3.js and vis.js, incorporating generative AI for assistance.

Additional Skills:

• Utilize generative AI prompts to accelerate learning and enhance algorithm creation.
• Visualize complex graphs in web environments for better comprehension and presentation.