Machine Learning Online Education Experience for Non-technical People: (Abstract Only)

ABSTRACT

With the increasing demand for understanding the theory of machine learning, professional people without technical background need to work with machine learning to solve the problem and to facilitate fostering work. The poster describes machine learning online education experience for professional industry people without technical background and machine learning knowledge. Firstly, the poster lists machine learning application requirements of professional people from various of industry area. Secondly, the poster analyzes the demand and typical characteristics of professional people. Based on above analysis, the poster proposes course framework including assignment, small projects and reading materials to assist professional people to understand the theory of machine learning and utilize Python machine learning open source framework to solve the problem in the industry and facilitate industry development. From the perspective of the application, the poster presents classical machine learning algorithms to help professionals to resolve collaborative filtering, classification, clustering, and optimization problem. Also, the poster introduces the instruction process of deep learning by using TensorFlow and teaches the student to understand complicated conception along with philosophy thinking. To broaden students/ horizons, a general-purpose reasoning system, Non-Axiomatic Reasoning System which is different from conventional reasoning systems is introduced. To help the student understand mathematics related conception, the author describes the instruction techniques including the graphical representation of knowledge, teaching with dialogue, to guide students to think deeply and to attract attention. To evaluate the objective of the course, the author summarizes the development progress, feedback from students and future improvement teaching action.

Index Terms

• Applied computing

  • Education

    • Interactive learning environments

• Computing methodologies

  • Machine learning