• Specialization: Intro to Statistical Learning 
  • Instructor: Osita Onyejeweke, Assistant Professor
  • Prior knowledge needed: Intro Statistics and Foundational Math

Learning Outcomes 

  • Understand the advantages and disadvantages of trees, and how and when to use them. 
  • Use SVMs for binary classification or K > 2 classes.
  • Find data representations via PCA and clustering.

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Course Content

In this module, you will learn about binary search trees and basic algorithms on binary search trees. We will also become familiar with the problem of balancing in binary search trees and study some solutions for balanced binary search trees such as Red-Black Trees.

In this module, you will learn about graphs and various basic algorithms on graphs such as depth first/breadth first traversals, finding strongly connected components, and topological sorting.

Union Find Data-structure with rank compression. Spanning trees and properties of spanning trees.Prim’s algorithm for finding minimal spanning trees. Kruskal’s algorithm for finding minimal spanning trees.

In this module, you will learn about:Shortest Path Problem: Basics.Bellman-Ford Algorithm for single source shortest path. Dijkstra’s algorithm.Algorithms for all-pairs shortest path problem (Floyd-Warshall Algorithm)

You will complete a programming assignment and multiple choice exam worth 25% of your grade. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.

Note: This page is periodically updated. Course information on the Coursera platform supersedes the information on this page. Click View on Coursera button above for the most up-to-date information.