0:15:24: Q1. Can h(X) be -ve here? ANS: yes
0:30:20: Q2. What is \(p_\Delta(x_\Delta)\) in terms of p(x)? ANS: \(p(x_\Delta) \Delta\)
0:49:12: Q3. What is the exponent of the pdf of Gaussian distribution with mean mu and variance sigma^2? ANS: \(-\frac{(x-\mu)^2}{2 \sigma^2}\)
0:59:07: Q4 What will be the second term simplified to? ANS: 1/2
1:07:49: Q5. How many bits are needed to store the heights of all 50 students? ANS: \(50 (\log \sqrt{2 \pi e (0.5) } - \log 0.1) = 243\) bits
1:36:57: Q6. What is Sigma_i,j (the i,j element of the covariance matrix) of multivariate Gaussian X? ANS: \(E[(X_i-E(X_i))(X_j-E(X_j))]\) OR covariance between \(X_i\) and \(X_j\)
1:40:34: Q7. What is the exponent of the multivariate Gaussian pdf? ANS: \(-\frac{1}{2}(x-\mu)^\top \Sigma^{-1} (x-\mu)\)
1:52:11: Q8. what is the summation over j? ANS: 1