Blog Post #9: EOTO 2, Machine Learning

     In this Age of A.I., the ability for machines and A.I. to learn in ways similar to humans has become something that has accelerated greatly in recent times. 

    Machine Learning is a branch of both computer science and A.I. that focuses on data and the use of algorithm to imitate learning like a human. The actual term was coined by American computer scientist Arthur Samuel. 

(Photo of Arthur Samuel and the IBM 7094)

        Samuels originally based it off of the game Checkers. The master of Checkers at the time, a man named Robert Nealy, played against the IBM 7094 computer in a game of Checkers and lost the match.

 (https://www.ibm.com/topics/machine-learning)

    Machine Learning is an important part of data science that uses statistical methods to create algorithms to make predictions, classifications, and uncover insights in data mining projects. These discoveries help make decisions concerning applications and businesses. The market for machine learning will expand over time as big data continues to expand. 

    

    Machine learning works in 3 steps:

    

    1. Decision Process: Machine Learning uses algorithms to make classifications and predictions. Based on some input data, the algorithm will predict a pattern.

    2. Error Function: It evaluates the prediction of the model. If there are already known examples, it can use those to assess the accuracy of the model. 

    3. Model Optimization Process: If the model can have a better fit for the data points, the model will repeat the "optimize and evaluate" process until the threshold of accuracy for the model is met. 

    Machine Learning has become incredibly popular as of late due to the popularity of A.I. in certain forms, with chatGPT being an example. Machine Learning is crucial component to any A.I., as an A.I. is supposed to learn though the 3 step process showcased in machine learning.