> In the first lesson, they discuss consistent hashing, and they seem to have achieved their goals.
I was really excited when I read your comment here before clicking the link, but having had a look at the rest of the curriculum, I'm slightly underwhelmed.
- Generalization ... Empirical risk minimization.
- lossy compression
- Similarity Search. (Dis)similarity metrics: Jaccard, Euclidean, Lp....
- Regularization. The polynomial embedding and random projection, L2 regularization, and L1 regularization as a computationally tractable surrogate for L0 regularization.
- Understanding Principal Component Analysis (PCA). ... The simple geometry of "diagonals in disguise." The power iteration algorithm.
- Low-rank matrix approximations. The singular value decomposition (SVD), applications to matrix compression, de-noising, and matrix completion (i.e. recovering missing entries).
- Graphs as matrices and the Laplacian of a graph. Interpretations of the largest and smallest eigenvectors/eigenvalues of the Laplacian. Spectral embeddings. Interpretations of the second eigenvalue
- Markov Chains, stationary distributions. Markov Chain Monte Carlo (MCMC)
- Fourier methods
- Compressive sensing
- Linear and convex programming. Matrix completion
- Differential privacy
I was thinking that the vast majority of those topics should be pretty much standard knowledge for a mathematically trained computer scientist (not being an expert in each of them, but knowing the basics of what they do, how they work and where they're applied). What level is this course at?