Investigating the Incorporation of Machine Learning Concepts in Data Structure Education

This Research to Practice Work-In-Progress paper discussed the incorporation of machine learning (ML) concepts in data structure education. The thriving of the ML especially deep learning techniques has led to an increased demand for trained professionals with ML skills to solve challenging engineering problems in many fields. Getting students familiar with ML as early as from CS2 (the data structure course) could benefit them in many aspects, but this direction has not been explored yet. In this paper, we discussed possible ways to integrate the ML concepts into data structure (DS) course. First, after introducing the concept of tensor in DS classroom teaching, we propose a practical experiment to implement the forward propagation of a pretrained convolutional neural network (CNN) aiming at classifying handwritten digits. Second, an experiment of decision tree based classification is set to give students an illuminating context via practicing the usage of tree structure. Finally, we design the experiment of computing graph to help the understanding of Directed Acyclic Graph (DAG), in which the students are required to implement the calculation of a multiple-variable function and its gradient based on DAG. Practicing DS knowledge in interesting ML-related problem contexts would intrigue the study enthusiasm of students and give them a general understanding of the application of DS knowledge in frontier technology, which could benefit the education of both DS and ML-related courses.