(article to be edited once i understand AI Machine Learning theories).
i've played with Java's Weka library for machine learning, for artificial intelligence.
While i didn't understand classification algorithms used, i was able to produce a code that learned how to classify & somehow classified 'plants' using the MultilayerPerceptron classifier. i read it's basicly the neural network, but considering my current knowledge - can't confirm.
i don't know if classification was succesful, as i don't know much about plants. but judging from data-nearness, it looked good.
i believe that experiments with code are very important parts of learning computer sciences, so i did this exercise despite my lacks in knowledge. hopefully it'll be useful for others as well.
i swallowed my own shame of not knowing theories, and posted this article for benefit of others.
There were other tools found such as assessing errors or adding weights to attributes consiering algorithms used, that i didn't understand. i think professional AI programmer should be fluent with all of these ideas & their uses.
Files: WekaTest.java, Datasets.
Library used: Weka.