Course Opening: Students, Goals, and Applications

Status: outline — expand with your first-session slides and demos.

Getting to know each other¶

• Backgrounds (math vs CS), programming comfort, expectations

• How the sessions run: concept + notebook demos

Goals of the course¶

• Shared mathematical language for data: representation, distance, structure, randomness, learning

• Applications first; math when it explains what you already saw

• Enough fluency to read BHK and later SML / ML courses

Example applications (demo list)¶

• Images as matrices (intro, segmentation, compression)

• kNN and k-means on real datasets

• Curse of dimensionality (why more features can hurt)

• SVD / PCA (faces, compression, ranking)

• Random graphs (networks, phase transition)

• Learning foundations (bias–variance, perceptron)

What is deferred¶

• Bayesian methods, HMM, MCMC $\rightarrow$ Statistical Machine Learning

• SVM / kernels $\rightarrow$ other ML course

Sources¶

• Course README and intro.md

• Blum–Hopcroft–Kannan, Foundations of Data Science (preface / Ch. 1)