How Does Machine Learning Work?
Dive into the rapidly emerging world of machine learning, where students come to understand the first attempts at developing the perceptron model—a simplified model of a biological neuron. Students also learn about the logic of the perceptron model and its limitations, which led to the development of multi-layer networks.
Does machine learning work like students learning in a classroom?Copyright 2020 Chris Tyler, Michigan State University RET
Machine learning is the process by which a computer is able to improve its own performance by continuously incorporating new data into an existing statistical model.
Computer scientists use machine learning to automate the process of data analysis in a variety of industries. In the financial sector, engineers apply principles of machine learning to engineer systems that can gain insights into complex data sets to prevent fraud. Healthcare professionals can use machine learning to help improve diagnoses and treatments. Computer scientists must understand the logic of the particular machine learning model and mathematics involved in machine learning in order to choose the appropriate model and parameters.
In the associated activity, Computer Simulation of an Artificial Neural Network, students play the role of the computer scientist by comparing the logic of two machine learning models including their strengths and weaknesses.
After this lesson, students should be able to:
- Define machine learning.
- List examples of how machine learning is used in different industries.
- Describe the process of using a machine learning model.
- Describe how the perceptron model learns.
- Describe the major weakness of the perceptron and how engineers overcome this weakness.