A coin classification system is created for a vending machine. An algorithm is presented with a large dataset containing images of coins which are labeled. The algorithm uses this data to learn decision boundaries that allow the vending machine to then classify each coin. O Supervised Learning Not a machine learning scenario Unsupervised Learning Reinforcement Learning

Consider a self-driving car travelling at 50 MPH. Suppose a stop sign recognition algorithm takes 15 seconds to classify a STOP sign with an acceptable accuracy, and it takes 5 seconds for the car to come to a complete stop once the break is applied, determine the distance (in feet) from the STOP sign should the recognition algorithm start analyzing the stop sign. (1 mile = 5280 feet)

A machine learning model is learning parameters w₁ and w₂. It turns out that for the latest data inputted, the error function is: E=(w₁-1)²+(W₂-1)² The current values of the parameters are w₁-2 and w₂=0.9. Gradient descent is applied to update the parameters. Which statement is true? 7 0 w1 2 W2 3 4 0 W2 W1 decreases and decreases, change in w₁ is bigger. W₁ decreases and w₂ increases, change in w₂ is bigger. W₁ decreases and w₂ increases, change in w₁ is bigger. W₁ decreases and w₂ increases, both by the same amount. 18 16 14 12 10 8 6 4 HH +++ 2