Machine Learning

Question 5 Suppose that for some linear regression problem (say, predicting housing prices as in the lecture), we have some training set, and for our training set we managed to find some θ0, θ1 such that J(θ0,θ1)=0. Which of the statements below must then be true? (Check all that apply.) For this to be true, …

Suppose that for some linear regression problem (say, predicting housing prices as in the lecture), we have some training set, and for our training set we managed to find some θ0, θ1 such that J(θ0,θ1)=0. Read More »

Question 4 Let f be some function so that f(θ0,θ1) outputs a number. For this problem, f is some arbitrary/unknown smooth function (not necessarily the cost function of linear regression, so f may have local optima). Suppose we use gradient descent to try to minimize f(θ0,θ1) as a function of θ0 and θ1. Which of …

Let f be some function so that f(θ0 θ1) outputs a number. For this problem f is some arbitrary/unknown smooth function (not necessarily the cost function of linear regression, so f may have local optima) Read More »

Machine Learning Week 1 Quiz 2 (Linear Regression with One Variable) Stanford Coursera Question 1 Consider the problem of predicting how well a student does in her second year of college/university, given how well she did in her first year. Specifically, let x be equal to the number of “A” grades (including A-. A and …

Machine Learning Week 1 Quiz 2 (Linear Regression with One Variable) Stanford Coursera Read More »

Question 1 Consider the problem of predicting how well a student does in her second year of college/university, given how well she did in her first year. Specifically, let x be equal to the number of “A” grades (including A-. A and A+ grades) that a student receives in their first year of college (freshmen …

Consider the problem of predicting how well a student does in her second year of college/university, given how well she did in her first year. Read More »