b) Principal Component Analysis
Consider 4 data points in the 2-d space: (-1,-1), (1.5, -1.5), (1, 1), (-1.5, 1.5).
1. Explain the first principal component? (Answer in the format of [a, b], round to 4 decimal places,
use positive values in the case of roots);
2. If we project all points into the 1-d subspace by the second principal components. What is the
variance of the projected data?
c) Explain the Q learning algorithm, for the warehouse locations and how that can be mapped to
different states.