Informal Response 1

02/05/2021

Question 1: In Laurence Maroney’s video, What is ML, he compares traditional programming with machine learning and argues that the main difference between the two is a reorientation of the rules, data and answers. According to Maroney, what is the difference between traditional programming and machine learning?

Laurence Maroney argued in his video that the main differnce between traditional programming and machine learning is whether we use rules to create answers or we use answers to create rules. He showed a diagram that explained that in traditional programming, data and rules are utilized to determine answers to whatever problem is attempting to be solved. Alternatively, in machine learning, data and the answers are being fed to create rules that can them be generalized.

Question 2: With the first basic script that Maroney used to predict a value output from the model he estimated (he initially started with 10 that predicted ~31. Modify the predict function to produce the output for the value 7. Do this twice and provide both answers. Are they the same? Are they different? Why is this so?

After modifying the script to produce the output for the value 7, I received predictions of 22.001947 and 22.001925. These values are not exactly the same, which makes sense since the models being built on the data are slightly different each time, but they are both similar and very close to the value 22, which is what you would predict if you used a mathematical formula instead.

Question 3: Using the script you produced to predict housing price, take the provided six houses from Mathews, Virginia and train a neural net model that estimates the relationship between them. Based on this model, which of the six homes present a good deal? Which one is the worst deal? Justify your answer.

After using the real-life houses to build the machine learning model, I used it to determine which prices were the best deals! The matthews, mobjack, and newptcomfort houses had actual prices that were very similar to the predicted prices so they were very reasonable. The hudgins house looks like the best price because the actual price was lower than the predicted. The church and moon houses were the worst deals because the actual prices are higher than the predicted.