Data Mining
Choose a Data Mining Activity
When you click the start button the right hand box will fill with words. Find the word “” drag it into the left hand box and click stop.
Ready to begin? Click start.
When you click the start button the right hand box will fill with all kinds of shapes and colors. Group all the “orange spheres” in the box below and click stop.
Ready to begin? Click start.
When you click the start button the right hand box will fill with all kinds of words. Click each time you see the word “”. When you have found them all, click stop.
Ready to begin? Click start.
dab dabble dace dipping dachshund dactyl dad daily daughter deal dean dear debate decal decay decide decorate definition degree diagram diamond dictate dictionary dip dime dine dirt disagree discard discharge divide docile dock doctor dog dolphin dominate donate done dose double drag drain draw drift drink drive drywall duck dull dipped durable Dutch duty dwarf dwell dye dynamite
Arthur Samuel was an IBM scientist who used the game of checkers to create the first learning program. He chose checkers because of its simplicity, compared to chess, which allowed him to focus on the learning method. His program became a better player after learning from many games against itself and a variety of human players in a supervised learning mode. In supervised learning, a program learns how to perform by copying the actions it ”observes“. In this case the program ”observed“ which moves were winning strategies and adapted its programming to incorporate those strategies. His checkers program was the first self-learning program which demonstrated the fundamental concept of machine learning.
Data collection and analysis has been performed for centuries, traditionally by the human hand and brain. As the amount of data increases in size and complexity, direct hands-on data collection and analysis is often replaced by electrons and silicon, in other words the computer. This has led to the creation of the term data mining. Data mining utilizes machine learning retrieval, sorting and analysis programs with the intention of uncovering patterns, finding and collecting specific data from a larger data set, as well as analyzing that data according to a certain criteria. This automated program provides the user, whether they are scientists, businesses, or government with reports detailing results of the analysis.
In the 2000's an explosion of adaptive programming (machine learning) became commonly used. Basically anywhere adaptive programs capable of recognizing patterns, learning from experience, abstracting new information from data, and optimizing the efficiency and accuracy of its processing and output are needed machine learning is there. Just think of all the potential applications where we might use machine learning in the coming decades.
