Titlebook: Mathematical Foundations for Data Analysis; Jeff M. Phillips Textbook 2021 Springer Nature Switzerland AG 2021 Data Analysis.Data Sciences

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開始發(fā)作

Convergence and Sampling,rk to think about how to aggregate more and more data to get better and better estimates. It will cover the . (CLT), Chernoff-Hoeffding bounds, Probably Approximately Correct (PAC) algorithms, as well as analysis of importance sampling techniques which improve the concentration of random samples.

繼承人

Distances and Nearest Neighbors,nd and the algorithms used. However, there are an enormous number of distances to choose from. We attempt to survey those most common within data analysis. This chapter also provides an overview of the most important properties of distances (e.g., is it a metric?) and how they are related to the dua

cloture

subordinate

誘使

背心

Clustering,re quite messy. And many techniques for clustering actually lack a mathematical formulation. We will initially focus on what is probably the cleanest and most used formulation: assignment-based clustering which includes .-center and the notorious .-means clustering. For background, we will begin wit

名義上

Pcos971

Antioxidant