Titlebook: Data Science for Transport; A Self-Study Guide w Charles Fox Textbook 2018 Springer International Publishing AG 2018 Quantitative Geography

slow-wave-sleep

兽皮

Bayesian Inference,d data. Bayesian inference is computationally hard, so typically works with approximate calculations on large compute systems. Bayesian inference is provably (Bernardo and Smith 2001) the . system able combine beliefs to make decisions consistently and optimally.

capsule

保留

白杨鱼

Python for Data Science Primer,. It may be skipped by readers who are interested only in concepts and management rather than the details of programming. If you already know Python and are able to complete the following skills check then you can also skip this chapter. If you are new to Python then try to complete the skills check

有权

ANTH

直言不讳

Machine Learning,y generative or causal models. We are given some known data pairs ., where . are features and . are discrete classes, then are asked to find the class . of single new data point . through some function .. Machine learning consists of finding the parameters ..

AXIS

GRIEF

Data Visualisation,e for the payoff: visualizing the results in full colour! This chapter will give a short overview of relevant human visual perception, present a “gallery” of classic transport-related data visualizations, then show how to produce some of your own.