I think the titles of lecture 8 and lecture 9 have been switched.

15:15 High likelihood and bad samples, garbage component is a constant in log-likelihood 40:00? Expectation on p data and p theta, how was this chosen 46:35 Note optimization of phi (discriminator) and theta (generator of fake samples) 50:45 Likelihood model in discriminator, but GANs can avoid likelihoods 1:00:15? Expectation on p data and p theta, added? 1:06:50 Minimax training objective 1:15:00 GANs no longer state of the art, very hard to train, mode collapse, no clean loss function to evaluate