I would however change the emphasis away from Gaussians to Poisson.

PS: Apparently I can’t embed videos here. Anyway, his thesis is that mathematical education over the past century has concentrated entirely on taking students to the pinnacle of being able to do Calculus and leave them there high and dry. Instead, he suggests that the ultimate goal of mathematical education should be statistics, which is of more practical use to more people. I don’t think he means to suggest that Calculus should not be taught — I couldn’t even understand propagation of errors without Calculus — but rather that the emphasis must shift to a more practical goal. I was brought up to believe that all the world is a partial differential equation, but even I can see that this is a sensible suggestion.

Mathematics:
Mathematics>Operations Research (lectures by Gene Golub, Professor at Stanford and Lieven Vandenberghe, one of the authors of Convex Optimzation – a link to the pdf file)
Mathematics>Statistics (including Peter Bickel, Professor at UC Berkeley).

Computer Science:
Computer Science>Bioinformatics
Computer Science>Data Mining
Computer Science>Data Visualisation
Computer Science>Image Analysis
Computer Science>Information Extraction
Computer Science>Information Retrieval
Computer Science>Machine Learning
Computer Science>Machine Learning>Bayesian Learning
Computer Science>Machine Learning>Clustering
Computer Science>Machine Learning>Neural Networks
Computer Science>Machine Learning>Pattern Recognition
Computer Science>Machine Learning>Principal Component Analysis
Computer Science>Machine Learning>Semi-supervised Learning
Computer Science>Machine Learning>Statistical Learning
Computer Science>Machine Learning>Unsupervised learning

Physics:
Physics (You’ll see Randall Smith)

[In the near future, some selected lectures with summary note might be suggested; nevertheless, your recommendations are mostly welcome.]