General ML: supervised vs unsupervised, K-means clustering, linear regression, logistic regression, maybe several enseble learning methods based on trees.
NNs: backpropagation, gradient descent, tensorflow, a bit about meta-param selection, CNNs (basically, just ImageNet), sometimes RNNs are mentioned.
This is all pretty entry-level and covered many times over, but, surprisingly, that's pretty much it. Discussion of models pretty much stops at ImageNet. I rarely see RBM or autoencoder, and pretty much nothing about how real problems are encoded into inputs and outputs.
I am ashamed to admit, but I still don't really understand how AlphaZero, AlphaStar or various language models (GPT, BERT) really work. Is there something good on that, maybe?